Programmatic tv advertising placement using cross-screen consumer data

ABSTRACT

The current invention relates to a computer-generated method for optimizing placement of advertising content to consumers&#39; TV&#39;s using a programmatic TV bidding model. The system can allocate advertising campaigns and plans to various inventory types based on the probability of accurate consumer matching. Consumer matching can be achieved by generation of look-alike models in a consumer&#39;s device graph to predict future consumption behavior. The system includes an interface through which an advertiser can access relevant information about inventory and success of a given placement.

CLAIM OF PRIORITY

This application is a continuation-in-part of U.S. application Ser. No.15/219,262, filed Jul. 25, 2016, which claims the benefit of priorityunder 35 U.S.C. §119(e) to U.S. provisional application Ser. No.62/196,592, filed Jul. 24, 2015, and 62/264,764, filed Dec. 8, 2015, allof which are incorporated herein by reference in their entirety.

RELATED APPLICATIONS

This application is related to U.S. patent application Ser. No.15/219,259, filed Jul. 25, 2016, entitled “TARGETING TV ADVERTISINGSLOTS BASED ON CONSUMER ONLINE BEHAVIOR” and having attorney docket no.2792-00-003U01, 15/219,268, filed Jul. 25, 2016, entitled “CROSS-SCREENMEASUREMENT ACCURACY IN ADVERTISING PERFORMANCE” and having attorneydocket no. 2792-00-005U01, 15/219,264, filed Jul. 25, 2016, entitled“SEQUENTIAL DELIVERY OF ADVERTISING CONTENT ACROSS MEDIA DEVICES” andhaving attorney docket no. 2792-00-006U01, and to provisionalapplication serial nos. 62/196,618, filed Jul. 24, 2015, 62/196,637,filed Jul. 24, 2015, 62/196,898, filed Jul. 24, 2015, 62/196,560, filedJul. 24, 2015, 62/278,888, filed Jan. 14, 2016, 62/290,387, filed Feb.2, 2016, and 62/317,440, filed Apr. 2, 2016, all of which areincorporated herein by reference in their entirety.

TECHNICAL FIELD

The technology described herein generally relates to improving andmanaging a cross-screen advertising strategy for advertisers, and moreparticularly relates to a system for targeting advertising content toconsumers on TV with a programmatic TV bidding model.

BACKGROUND

Video advertisements are among the most advanced, complicated, andexpensive, forms of advertising content. Beyond the costs to producevideo content itself, the expense of delivering video content over thebroadcast and cable networks remains considerable, in part becausetelevision (TV) slots are premium advertising space in today's economy.Furthermore, TV is no longer a monolithic segment of the media market.Consumers can now spread their viewing of video content, particularlypremium content, across TV, DVR, and a menagerie of over-the-top andon-demand video services viewed across smart TVs, gaming consoles, andmobile devices, as well as traditional TVs.

In short, TV viewing is transforming to digitally distributed viewing,as audiences watch proportionately less live broadcasting and more in avideo on demand (VOD) or streaming video format. Correspondingly,distributors of TV content are looking more and more to harness digitaldata from TVs themselves to offer more attractive placement options toadvertisers.

Adding online consumption to the list of options available to any givenconsumer, only adds greater complexity to the process of coordinatingdelivery of video adverts to a relevant segment of the public. Thiscomplexity means that the task of optimizing delivery of advertisingcontent today far exceeds what has traditionally been necessary, andwhat has previously been within the capability of experienced persons.The data needed to fully understand a given consumer is fragmented aseach individual and household views more and more media in a disparatefashion by accessing a network of devices.

Nevertheless, for many companies, the analytical work that goes intodeveloping an advertising strategy today still requires manualcontributions by human analysts. This is especially the case for lowvolume purchasers of advertising inventory, and is largely the case foradvertising purchases on “linear TV”—TV content that is pre-scheduled(often many weeks or months in advance), as opposed to video-on-demand.

Advertising strategies are also generally fixed, meaning that approachesto advertising strategy are conditioned on certain assumptions that areinflexible and limited to what manual processes are able to achieve. Thecurrent state of advertising strategy is analogous to where financialtrading was before the creation of financial strategy tools such asE-Trade, which facilitates automated buying, and financial advisors suchas Fidelity for investment planning.

In implementing today's advertising strategies, human analysts guide theselection of advertising inventory based on, for example, Excel datatables and other static data management tools. This results ininefficient selection of slots, and delays in responding to markettrends. Consumers are not disparate silos of preference based on thedevice they are using, but the market for advertising treats them thatway due to limitations in the available technology tools, most of whichare incapable of quickly and accurately integrating disparate data sets.For example, today, TV consumption data exists separately from set topbox owner data and TV OEMs. It follows that advertising strategies forTV are planned according to TV-specific criteria, and web and mobileadvertising, which include sub categories such as social media, are eachplanned separately. Across the advertising industry, there are separateentities planning for different media platforms, such as set-top box,phone and desktop. Across the different media there exists disparatedata, data systems, and data sources (vendors). Today, these device andmedia categories remain largely segmented when incorporated intoadvertising campaign strategies and planning. Nevertheless, theincreasing availability of digital TV data, often referred to as“programmatic TV” data, means that advertisers are beginning to be ableto be more sophisticated about their purchases of TV ad slots.

Currently, some companies attempt to link together a set of devicesrelated to a particular consumer, but they are not capable of treatingthe disparate data sources with any reliable level of data integration,or at a scale that is useful to advertisers. Identifying selections ofdevices by comparing and modeling incomplete user data against that ofother similar users within the market segment is a partial solution tothis issue, but current methods are not able to create associations at alevel of granularity that is reliable or useful.

Today, probabilistic and deterministic methods are not widely utilizedto associate mobile and computer devices to a precise audience, orhousehold. One reason such methods are not more widely adopted isbecause of inefficient processing and pairing of data across differentdevices. For example, in order to predict consumer purchasing, viewing,and advertising interaction habits at a 1:1 level of an associationbetween a user and their respective device, it is insufficient to assumethat any single instance of device access is representative of thatuser's purchasing intent. This is due to modern day habits of mediaconsumption—users consume media on a large variety of devices as well asvia different media (such as Hulu, Netflix, or cable television). Assuch, a much more complex analysis that enables insight into theintersection of media consumption and a user's family of devices isnecessary.

Another reason probabilistic and deterministic methods are not morereadily available to gauge consumer purchasing habits is because accessto user device data is not easily achieved. For example, under consumerprivacy laws, it is unlawful to access a user's device without theirexplicit consent. Therefore, on a mass scale, it is often unknown whatcombination of devices a demographic of users use, and what media theyconsumed on their respective devices. This poses a large challenge foradvertisers when determining which advertising inventory to purchase andhow best to reach their target audience efficiently on a given categoryof device, in particular the TVs they watch.

Today, the data systems that track consumer information for use inadvertising targeting lack the ability to broadly combine and integratetransmutable and non-transmutable categories (i.e., requiring theintegration of a plurality of membrane levels) of consumer data. Mostdata systems contain static, one-dimensional, homogeneousclassifications of consumers. For example, a 29-year old who bought acar two years ago will be a consumer data point that will not adjust orbe updated over time. While adjusting the age of this individual overtime is simple, other transmutable characteristics such as desire to getmarried, pregnancy, or other lifestyle changes are not easy to assess orpredict. This translates to an inefficiency of advertising placement,and also means that even if an advertiser can target online content tothat consumer, their ability to reach them on the TVs they view isseverely restricted.

Accordingly, there is a need for a method of integrating and connectingdata on a given consumer that is acquired over time from multipledifferent devices, and to use that integrated data in making reliableplacement of advertising content across multiple devices, in particularfor TV advertisement slots.

The discussion of the background herein is included to explain thecontext of the technology. This is not to be taken as an admission thatany of the material referred to was published, known, or part of thecommon general knowledge as at the priority date of any of the claimsfound appended hereto.

Throughout the description and claims of the application the word“comprise” and variations thereof, such as “comprising” and “comprises”,is not intended to exclude other additives, components, integers orsteps.

SUMMARY

The instant disclosure addresses the processing of consumer andadvertising inventory in connection with optimizing placement ofadvertising content across display devices, in particular in aprogrammatic TV environment. The disclosure comprises methods for doingthe same, carried out by a computer or network of computers. Thedisclosure further comprises a computing apparatus for performing themethods, and computer readable media having instructions for the same.The apparatus and process of the present disclosure are particularlyapplicable to video content in online and TV media.

In overview, the present method allows an advertiser to bid on TV slotsbased on a probability of matching to an audience category or type. Inparticular, the present technology relates to systems and methods foroptimizing advertising campaigns via programmatic TV bidding processes.The methods herein lend themselves to increasing return on investment ofmoney spent on advertising by increasing efficiency and reducing costsassociated with identifying an advertising strategy.

The systems can operate at high-frequency, such as running from 10,000to 100,000 queries of audience impressions per second. The queries canbe dynamic, and in real-time.

In an alternative embodiment, the system develops an advertisingstrategy designed around specified campaign (end-user) parameters. Thestrategy is directed to advertising occurring on TV, particularly usingprogrammatic TV bidding methods.

The method involves analyzing consumer, media and related data, from anunlimited number of data inputs, including but not limited to:behavioral such as specific viewing and purchasing histories ofindividual consumers, as well as demographic, and location-relatedsources.

The present technology includes programmatic generation of look-alikemodels in a consumer's device graph to predict future consumptionbehavior. The method integrates actual content consumption behavior withbroadcasted user segments, as well as assigning the devices used forconsumption to a single consumer.

The present disclosure provides for a method for targeting delivery ofadvertising content to a population of consumers across one or moreslots in television programming, the method comprising: receiving apricepoint and one or more campaign descriptions from an advertiser,wherein each of the campaign descriptions comprises a schedule fordelivery of an item of advertising content across one or moretelevisions accessed by a consumer in the population of consumers, and atarget audience, wherein the target audience is defined by one or moredemographic factors; defining a pool of consumers based on a graph ofconsumer properties, wherein the graph contains information about two ormore TV and mobile devices used by each consumer, demographic and onlinebehavioral data on each consumer, and similarities between pairs ofconsumers, and wherein the pool of consumers comprises consumers havingat least a threshold similarity to a member of the target audience;receiving a list of inventory from one or more content providers,wherein the list of inventory comprises one or more TV slots;identifying one or more advertising targets, wherein each of the one ormore advertising targets comprises one or more TV slots that are to bedelivered within TV content identified as likely to be viewed by thepool of consumers, consistent with one or more of the campaigndescriptions, and an overall cost consistent with the pricepoint;bidding on one or more of the TV slots in an advertising target; and ifthe bidding results in a success or a hold, instructing a media conduitto deliver the item of advertising content within the one or more TVslots to a consumer in the population of consumers on a television.

The present disclosure further provides for computer readable media,encoded with instructions for carrying out methods described herein andfor processing by one or more suitably configured computer processors.

The present disclosure additionally includes a computing apparatusconfigured to execute instructions, such as stored on a computerreadable medium, for carrying out methods described herein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows, diagrammatically, relationships between parties thatcontribute to delivery of advertising content, such as advertisers, anadvertising exchange, media conduits, and consumers.

FIG. 2 shows a consumer graph.

FIG. 3 shows a node in a graph.

FIGS. 4A and 4B show steps in creation of a consumer graph.

FIG. 5 shows relationships between various entities in the advertisingpurchase realm.

FIG. 6 shows a multi-dimensional data set.

FIG. 7 shows a flow-chart of a process as described herein.

FIGS. 8A, 8B show an exemplary computer interface for an embodiment.

FIG. 9 shows an apparatus for performing a process as described herein.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

The instant technology is directed to computer-implemented methods thatcombine actual content consumption behavior, segments of the consumerpopulation, and an assignment of the devices used for media consumption,in order to assist in optimal placement of programmatic TV advertisingcontent. The methods provide utility for advertisers, content owners,brand managers, data platforms, buying platforms, market researchcompanies, wireless carriers, TV manufacturers, pay-TV operators, TVbroadcasters, and the like.

Advertising Functions

Relationships between entities in the business of purchase, delivery andconsumption of advertising content are depicted in FIG. 1. As can beseen, the advertising ecosystem is complex, involves many differententities, and many different relationships.

An advertiser 101 is a purchaser of advertising inventory 109. Anadvertiser may be a corporation that exercises direct control over itsadvertising functions, or it may be an agency that manages advertisingrequirements of one or more clients, usually corporate entities. Theadvertiser intends to make advertising content 103 (also an“advertisement” herein) available to one or more, typically a populationof, consumers 105, on one or more devices 107 per consumer, including atleast one TV. Creators of advertising content are not shown in FIG. 1,and may contract separately with advertisers or their agencies 101.

Devices include, for a given consumer, one or more of: TV's (includingSmartTV's, or web-enabled TV's), mobile devices (cell phones,smartphones, media players, tablets, notebook computers, laptopcomputers, and wearables), desktop computers, networked photo frames,set top boxes, gaming consoles, streaming devices, and devicesconsidered to function within the “Internet of Things” such as domesticappliances (fridges, etc.), and other networked in-home monitoringdevices such as thermostats and alarm systems. The TV's availableinclude at least one TV that is able to offer programmatic TV data, asfurther described herein.

The advertising content 103 has typically been created by the advertiser101 or a third party with whom the advertiser has contracted, andnormally includes video, audio, and/or still images that seek to promotesales or consumer awareness of a particular product or service.Advertising content 103 is typically delivered to consumers 105 via oneor more intermediary parties, as further described herein.

Advertising content is typically of two different types: branding, anddirect-response marketing. The timeframe is different for these twotypes. Branding promotes awareness; direct response marketing isdesigned to generate an immediate response. For example, an automobilemanufacturer may put out direct response marketing material into themarket place, and wants to measure responses by who went to a dealershipor website after seeing an advertisement. The methods herein can beapplied to both types of advertising content, but the measurement ofeffectiveness is different for the two types: for example, effectivenessof branding is measured by GRP's (further described elsewhere herein),and results of direct response marketing can be measured by, forexample, website visits in the wake of the display of a particularadvertisement.

When delivered to a mobile device such as a phone or a tablet,advertising content 103 may additionally or alternatively take the formof a text/SMS message, an e-mail, or a notification such as an alert, abanner, or a badge. When delivered to a desktop computer or a laptopcomputer or a tablet, the advertising content 103 may display as apop-up within an app or a browser window, or may be a video designed tobe played while other requested video content is downloading orbuffering. When delivered to a TV, advertising content 103 is typicallyin video format, in whole or in part.

Consumers 105 are viewers and potential viewers of the advertisingcontent 103 and may have previously purchased the product or servicethat is being advertised, and may—advantageously to the advertiser—belearning of the product or service for the first time when they view theadvertising content 103.

Advertising inventory 109 (also inventory or available inventory,herein) comprises available slots, or time slots 117 (also referred toherein as a spot), for advertising across the several media interfaces,or conduits 111, through which consumers access information andadvertising content. Such media interfaces include TV (including linearand programmatic TV, as further described herein), radio, social media(for example, online networks, such as LinkediN, Twitter, Facebook),digital bill boards, mobile apps, and the like. Media conduits 111 maygenerate their own content 113, or may be broadcasting content from oneor more other content providers or publishers 115. For example, a cablecompany is a media conduit that delivers content from numerous TVchannel producers and publishers of content. Media interfaces may alsobe referred to as content providers, generally, because they delivermedia content 113 (TV programs, movies, etc.) to consumers 105. Oneaspect of the technology herein includes the ability to bid onprogrammatic TV inventory in order to deliver advertising content to aparticular demographic of TV viewers at particularly favorable timeslots. Media conduits 111 also deliver advertising content 103 that hasbeen purchased for delivery at time slots 117, to consumers 105 forviewing on various devices 107. A publisher 115 is typically a contentowner (e.g., BBC, ESPN).

A slot 117 is a time, typically expressed as a window of time (1 minute,2 minutes, etc.) at a particular time of day (noon, 4:30 pm, etc., or awindow such as 2-4 pm, or 9 pm-12 am), or during a specified broadcastsuch as a TV program, on a particular broadcast channel (such as a TVstation, or a social media feed). A slot may also be defined by broadercategories used by the TV industry such as the daypart (breakfast,daytime, prime time, late night, or overnight). A slot may be furtherdefined by a region of broadcast, such as a DMA (designated marketarea). An available slot is a slot in the inventory that an advertisermay purchase for the purpose of delivering advertising content.Typically it is available because another advertiser has not yetpurchased it. The term slot may be used interchangeably with spot,herein.

As further described herein, a slot may additionally be defined bycertain constraints such as whether a particular type of advertisingcontent 103 can be delivered in a particular slot. For example, a sportsequipment manufacturer may have purchased a particular slot, defined bya particular time of day on a particular channel, and may have alsopurchased the right to exclude other sports equipment manufacturers frompurchasing slots on the same channel within a certain boundary—intime—of the first manufacturer's slot. In this context, a “hardconstraint” is a legal or otherwise mandatory limitation on placingadvertising in particular time slots or within specified media. A “softconstraint” refers to desired (non-mandatory) limitations on placingadvertising in particular time slots within specified media. “Constraintsatisfaction” refers to the process of finding a solution to a set ofconstraints that impose conditions that the variables must satisfy. Thesolution therefore is a set of values for the variables that satisfiesall constraints.

Information is intended to mean, broadly, any content that a consumercan view, read, listen to, or any combination of the same, and which ismade available on a screen such as a TV screen, computer screen, ordisplay of a mobile device such as a tablet, smart-phone, orlaptop/notebook computer, a wearable such as a smart-watch, fitnessmonitor, or an in-car or in-plane display screen. Information isprovided by a media interface 111 such as a TV or radio station, amulti-channel video programming distributor (MVPD, such as a cable TVprovider, e.g., Comcast), or an online network such as Yahoo! orFacebook.

The communication between the advertisers and the media conduits can bemanaged by up to several entities, including: a demand-side provider(DSP) 123, an advertising exchange 119, and a supply-side provider 121.An advertising exchange 119 (also, exchange herein) is an environment inwhich advertisers can bid on available media inventory, which caninclude TV as well as mobile devices. The TV inventory of interestherein may be digital such as programmatic TV, or may be analog, such asvia a TV channel such as ESPN, CNN, Fox, or BBC, or an FM/AM radiobroadcast. An advertising exchange 119 typically specializes in certainkinds of content. For example, SpotX specializes in digital content,whereas WideOrbit specializes in programmatic TV.

Supply-side provider (SSP) 121 is an intermediary that takes inventory109 from a media conduit 111, and makes it available to a demand-sideprovider (DSP) 123, optionally via exchange 119, so that advertisers canpurchase or bid on the inventory when deciding how to positionadvertising content 103. SSP's have sometimes been categorized as publicor private depending on whether a media conduit is able to limit theidentity and number of advertisers who have access to the inventory. Insome situations, an SSP interacts directly with a DSP without the needfor an advertising exchange; this is true if the functions of anadvertising exchange that a purchaser of advertising content relies onare performed by one or both of the DSP and SSP. The technology hereinis particularly suited for being implemented and being carried out by asuitably-configured DSP and assists in its purchase of advertising viaprogrammatic TV content.

In one configuration, an advertising exchange 119 interfaces between asupply side provider (SSP) 121 and a demand side provider (DSP) 123. Theinterfacing role comprises receiving inventory 109 from one or moreSSP's 121 and making it available to the DSP, then receiving bids 125 onthat inventory from the DSP and providing those bids 125 to the SSP.Thus, a DSP makes it possible for an advertiser to bid on inventoryprovided by a particular SSP such as SPotX, or WideOrbit. In someconfigurations, the DSP takes on most or all of the role of anadvertising exchange.

An advertising campaign (or campaign) is a plan, by an advertiser, todeliver advertising content to a particular population of consumers. Acampaign will typically include a selection of advertising content (suchas a particular advertisement or various forms of an advertisement, or asequence of related advertisements intended to be viewed in a particularorder), as well as a period of time for which the campaign is to run(such as 1 week, 1 month, 3 months). An advertiser typically transmits acampaign description 127 to an advertising exchange 119 or a DSP 121,and in return receives a list of the inventory 109 available. A campaigndescription 127 may comprise a single item of advertising content 103and one or more categories of device 107 to target, or may comprise aschedule for sequential delivery of two or more items of advertisingcontent 103 across one or more devices 107. A campaign description 127may also comprise a description of a target audience, wherein the targetaudience is defined by one or more demographic factors selected from,but not limited to: age range, gender, income, and location.

The DSP 123 then provides an interface by which the advertiser 101 canalign its campaign descriptions 127 against inventory 109 and purchase,or bid on, various slots 117 in the inventory. The DSP 123, or anexchange 119, may be able to provide more than one set of inventory thatmatches a given campaign description 127: each set of inventory thatmatches a given campaign description is referred to herein as anadvertising target 129. The advertiser 101 may select from among a listof advertising targets, the target or targets that it wishes topurchase. Once it has purchased a particular target, the SSP 121 isnotified and delivery instructions 137 are sent to the various mediaconduits 111 so that the advertising content 103 can be delivered in theapplicable time slots 117, or during selected content 113, to therelevant consumers.

When seeking programmatic TV content, a purchase of a given slot is notsimply a straightforward sale at a given price, but is achieved via abidding process. The DSP will place bids on a number of slots, and foreach one, will have identified a bid price that is submitted to the SSP.For a winning bid, the SSP delivers the advertising content to the mediaconduit, and ultimately the consumer. Bids are generally higher forspecific targeting than for blanket targeting.

The bidding process depends in part on the type of advertising content.TV content can be scheduled in advance, whereas for online content, thetypical bid structure is ‘just-in-time’ bidding: the advert is deliveredonly if a particular consumer is seen online. In general, the methodsherein are tailored to programmatic TV bidding processes, and areapplicable to any of the bidding methods that may be deployed, and thatexploits details of programmatic TV data.

The process of generating advertising targets may also depend one ormore campaign requirements. A campaign requirement, as used herein,refers to financial constraints such as a budget, and performancespecifications such as a number of consumers to target, set by anadvertiser or other purchaser of advertising inventory. Campaignrequirement information is used along with campaign descriptions whenpurchasing or bidding on inventory.

DSP's 123 also provide advertisers 101 with data on consumers anddevices, aggregated from various sources. This data helps an advertiserchoose those time slots and media conduits from the inventory that willbest suit its goals.

Data used by DSP's may include census data 131, or data on specificconsumers and devices 133. Census data 131 includes data on a populationthat can be used to optimize purchase of inventory. Census data 131 cantherefore include demographic data such as age distribution, incomevariations, and marital status, among a population in a particularviewing region independent of what media interfaces the members of thepopulation actually view. Census data 131 can be aggregated from avariety of sources, such as state and county records, and U.S. CensusBureau data.

A data management platform (DMP) 135 can provide other types of thirdparty data 133 regarding consumers and the devices they use to the DSP.Typically a DMP provides a data warehousing facilities with embeddedfunctionality. DMPs download data and can perform a variety ofanalytical functions ranging from sorting, storing, processing, applyingmatching algorithms, and providing data outputs to purchasers andsubscribers. Examples of DMP's include: Krux, Exelate, Nielsen, Lotame.The consumer and device data 133 that is delivered to a DSP from a thirdparty provider may complement other consumer and device data 143 that isprovided by the media conduits. Data on consumers and the devices theyuse that is relevant to an advertiser includes matters of viewing habitsas well as specific behavioral data that can be retrieved directly froma media conduit. For example, as further discussed elsewhere herein,when a media conduit serves an advertisement to a consumer, the conduitcan collect information on that user's manner of access to the advert.Due to the volume of data involved, after a relatively short period oftime, such as 14 days, a media conduit may not be able to furnish anyinformation on a particular consumer. In that instance, the DSP can getdata on that user from a third party such as a DMP. Third parties canget data offline as well. As used herein, an offline event is one thathappens independently of the Internet or a TV view: for example, it caninclude purchase of an item from a store and other types oflocation-based events that an advertiser can view as significant. Datacan be shared between the entities herein (e.g., between a DMP and aDSP, and between DSP and SSP, and between media conduits and a SSP oradvertising exchange) using any commonly accepted file formats forsharing and transfer of data: these formats include, but are not limitedto: JSON, CSV, and Thrift, as well as any manner of text fileappropriately formatted.

An impression refers to any instance in which an advertisement reaches aconsumer. On a TV, it is assumed that if the TV is broadcasting theadvertisement then an individual known to be the owner of, or a regularviewer of, that TV will have been exposed to the advertisement, and thatdisplay counts as an impression. If multiple persons are in the samehousehold then the number of impressions may equal the number of personswho can view that TV. In the online environment, an impression occurs ifa consumer is viewing, say, a web-page and the advertisement isdisplayed on that web-page such as in the form of a pop-up, or if theuser has clicked on a link which causes the advertisement to run.

An audience segment is a list of consumers, de-identified from theirpersonally identifiable information using cookie syncing or othermethods, where the consumers belong to a type (income, gender,geographic location, etc.), or are associated with a behavior:purchases, TV viewership, site visits, etc.

Cookie syncing refers to a process that allows data exchange betweenDMP's SSP's and DSP's, and more generally between publishers of contentand advertisement buyers. A cookie is a file that a mobile device ordesktop computer uses to retain and restore information about aparticular user or device. The information in a cookie is typicallyprotected so that only an entity that created the cookie cansubsequently retrieve the information from it. Cookie syncing is a wayin which one entity can obtain information about a consumer from thecookie created by another entity, without necessarily obtaining theexact identify of the consumer. Thus, given information about aparticular consumer received from a media conduit, through cookiesyncing it is possible to add further information about that consumerfrom a DMP.

For mobile devices, there is a device ID, unique to a particular device.For TV's there is a hashed IP address. The device ID information may beused to link a group f devices to a particular consumer, as well as linka number of consumers, for example in a given household, to a particulardevice. A DSP may gather a store of data, built up over time, inconjunction with mobile device ID's and TV addresses that augment‘cookie’ data.

Cross-screen refers generally to analysis of media, consumer, and devicedata that combines viewer data across multiple devices. Cross-screenencompasses distribution of media data, including advertising content,across multiple devices of a given consumer, such as a TV screen,computer screen, or display of a mobile device such as a tablet,smart-phone or laptop/notebook computer, a wearable such as asmart-watch or fitness monitor, or an in-car, or in-plane displayscreen, or a display on a networked domestic appliance such as arefrigerator.

Reach is the total number of different people exposed to anadvertisement, at least once, during a given period.

In a cross-screen advertising or media campaign, the same consumer canbe exposed to an advertisement multiple times, through different devices(such as TV, desktop or mobile) that the consumer uses. Deduplicatedreach is the number of different people exposed to an advertisementirrespective of the device. For example, if a particular consumer hasseen an advertisement on his/her TV, desktop and one or more mobiledevices, that consumer only contributes 1 to the reach.

The incremental reach is the additional deduplicated reach for acampaign, over and above the reach achieved before starting thecampaign, such as from a prior campaign. In one embodiment herein, atype of campaign can include a TV extension: in this circumstance, anadvertiser has already run a campaign on TV, but is reaching a point ofdiminished returns. The advertiser wants to find ways to modify thecampaign plan for a digital market, in order to increase the reach. Inthis way, a DSP may inherit a campaign that has already run its courseon one or more media conduits.

VOD refers to video on demand systems, which allow users to select andwatch or listen to video or audio content when they choose to, ratherthan having to watch content at a scheduled broadcast time. Internettechnology is often used to bring video on demand to televisions andpersonal computers. Television VOD systems can either stream contentthrough a set-top box, a computer or other device, allowing viewing inreal time, or download it to a device such as a computer, digital videorecorder (also called a personal video recorder) or portable mediaplayer for viewing at any time.

The term “Programmatic TV” (PTV) is used widely in the industry, and canrefer to a number of different types of automated methods of biddingfor, and positioning, advertisements within, TV content. Theadvertisements include digital TV advertisements delivered online toconnected TVs, as well as linear TV ads delivered via set-top boxes.Programmatic TV technology typically relies on access to consumer dataof varying types, including but not limited to IP address data and TVviewing data that can be associated with a particular demographic oreven an individual consumer. As described herein, methods of bidding onPTV data can rely on data on consumers that has been gathered from otherdevices, i.e., cross-screen data.

The bidding on PTV content is an important aspect of its operationbecause it permits dynamic, or close-to-dynamic, bidding in anenvironment that has traditionally been restricted to arrangements forpre-purchasing slots (such as 1-2 weeks in advance). PTV introduces thepossibility of an automated bidding process into the purchase ofinventory, in particular one in which the price of inventory can beautomatically determined.

One category of demographic data for PTV is “one on one” when it ispossible to associate a given consumer with a particular TV (such datamay be available from companies such as Vizio). A second category ofdemographic data for PTV includes a group of persons, such as 25 or morepersons, in a defined cluster from which it is not possible to identifya given individual or household but such that the cluster issufficiently well-defined that an advertiser would wish to targetadvertising content to them. For example, the demographic may be definedby a combination of age, income, location, and a specific recentshopping experience. A third category of demographic is less specificbut is easy for an MVPD to target: for example it may be definedgeographically such as by a zip code. Still other demographic data maybe expressed at a higher level by region, such as national, or by DMA.

The term PTV may also be used interchangeably with other terms such as“addressable TV” (wherein an MVPD can tailor content to a specific TV,associated with a known consumer), “TV-on-demand”, and “over the top”TV, as further described herein. In some contexts, other specificdelivery vehicles such as “Apple TV” can encompass elements of PTV. Theinterchangeability of such terms arises from an ability to targetadvertising content in a specific manner, by using a bidding process.

PTV is important because it shortens the time for realizing the resultof a bid: a purchaser of advertising inventory will know very quicklywhether a bid on PTV delivered content was successful, even where theslot in question is not for several days or a week into the future.Conversely, PTV also provides a greater period of time by which to bidahead on content, in the sense that it makes it possible for anadvertiser to continue to bid till much closer to the time that aprogram will air than previously. In this way, it gives an advertiser anopportunity to bid on unsold inventory even when it is close to thebroadcast time.

In addition to TV programming content, and online content delivered todesktop computers and mobile devices, advertisements may be deliveredwithin OTT content. OTT (which derives from the term “over the top”)refers to the delivery of audio, and video, over the Internet withoutthe involvement of a MVPD in the control or distribution of the content.Thus, OTT content is anything not tied to particular box or device. Forexample, Netflix, or HBO-Go, deliver OTT content because a consumerdoesn't need a specific device to view the content. By contrast, MVPDcontent such as delivered to a cable or set top box is controlled by acable or satellite provider such as Comcast, AT&T or DirectTV, and isnot described as OTT. OTT in particular refers to content that arrivesfrom a third party, such as Sling TV, YuppTV, Amazon Instant Video,Mobibase, Dramatize, Presto, DramaFever, Crackle, HBO, Hulu, myTV,Netflix, Now TV, Qello, RPI TV, Viewster, WhereverTV, Crunchyroll or WWENetwork, and is delivered to an end-user device, leaving the Internetservice provider (ISP) with only the role of transporting IP packets.OTT delivered content is important because of the feedback data that itprovides, which in turn can be used to refine information about consumerdemographics.

Furthermore, an OTT device is any device that is connected to theinternet and that can access a multitude of content. For example, Xbox,Roku, Tivo, Hulu (and other devices that can run on top of cable), adesktop computer, and a smart TV, are examples of OTT devices.

Gross rating point (GRP) refers to the size of an advertising campaignaccording to schedule and media conduits involved, and is given by thenumber of impressions per member of the target audience, expressed as apercentage (GRP can therefore be a number >100. For example, if anadvert reaches 30% of the population of L.A. 4 times, the GRP is 120.(The data may be measured by, e.g., a Nielsen panel of say 1,000 viewersin L.A.).

The target rating point (TRP) refers to the number of impressions pertarget audience member, based on a sample population. This numberrelates to individuals: e.g., within L.A. the advertiser wants to targetmales, 25 and older. If there are 100 such persons in the L.A. panel and70% saw the ad., then the TRP is 70%×number of views.

“High frequency” refers to high frequency trading related to advertisingpurchases and sales. The methods and technology herein can be practicedby trading platforms for advertising that utilize computers to transacta large number of bid requests for advertising inventory at high speeds.

Consumer Data

Data about consumers can be categorized into two groups: there arenon-transmutable characteristics such as ethnicity, and gender; andthere are transmutable characteristics such as age, profession, address,marital status, income, taste and preferences. Various transmutablecharacteristics such as profession are subject to change at any time,while others such as age change at a consistence rate. Today, the datasystems that track consumer information for use in targeting advertisingcontent lack the ability to broadly track both categories of consumerdata. Most data systems contain static, homogenous classifications ofconsumers. For example, a 29-year old who bought a car two years agowill be a consumer data point that will not be updated or augmented withtime. Even if the age of the individual as stored in a system can beadjusted with time, other transmutable characteristics such as change inmarital state, or lifestyle changes, are not taken into account in thisconsumer's classification.

At various stages of the methods herein, it is described that eachconsumer in a population of consumers is treated in a particular way bythe method: for example, a computer may be programmed to analyze data oneach consumer in its database in order to ascertain which, if any, haveviewed a particular TV show, or visited a particular website;alternatively, some comparative analysis may be performed, in whichattributes of each user in one category of population is compared withattributes of each consumer in another category of population. Eachpopulation set may comprise many thousands of individuals, or manyhundreds of thousands, or even millions or many millions of individuals.It is assumed herein that the methods, when deployed on suitablecomputing resources, are capable of carrying out stated calculations andmanipulations on each and every member of the populations in question.However, it is also consistent with the methods herein that “eachconsumer” in a population may also mean most consumers in thepopulation, or all consumers in the population for whom the statedcalculation is feasible. For example, where one or more given consumersin a population is omitted from a particular calculation because thereis insufficient data on the individual, that does not mean that aninsufficient number of members of the population is analyzed in order toprovide a meaningful outcome of the calculation. Thus “each” whenreferencing a population of potentially millions of consumers does notnecessarily mean exactly every member of the population but may mean alarge and practically reasonable number of members of the population,which for the purposes of a given calculation is sufficient to produce aresult.

Consumer Graph

A consumer graph is a graph in which each node represents a consumer (orindividual user). The technology utilizes various implementations of aweighted graph representation in which relationships between consumers(nodes) are defined as degrees of similarity (edges). A consumer graphis used herein to categorize, store, and aggregate large amounts ofconsumer data, and allow an entity such as a DSP to make connectionsbetween data used to build a consumer graph with other data—such as TVviewing data—via data on given consumers' devices. The consumer graph isbased on cross-screen data and can be used to identify a population ofconsumers to which programmatic TV advertising content is to betargeted.

One way to construct the graph is by using deterministic relationshipdata; another is probabilistically using the attributes of each node. Insome instances, a combination of deterministic and probabilistic methodscan be used. In a deterministic, approach, which is relativelystraightforward, the basis is having exact data on a consumer, such aslogin information from a publisher. Thus, if a person has logged inmultiple times on different devices with the same ID, then it ispossible to be sure that the person's identity is matched. However, suchexact information may not always be available. By contrast, in aprobabilistic approach, it is necessary to draw inferences: for example,if the same device is seen in the same location, or similar behavior canbe attributed to a given device at different times, then it possible toconclude that the device belongs to the same user.

In some embodiments herein, machine learning methods, and Bayesian andregression algorithms, are used to explore commonalities betweenconsumers. Such methods are useful in situations where there is a finitenumber of parameters to be considered. In some other embodiments,techniques of deep learning are more useful in finding consumersimilarities and constructing a consumer graph. Machine learning is apreferred technique for matching exact pieces of information, forexample whether the same websites have been visited by two consumers,but deep learning can explore the details of a particular video or TVprogram—for example, by analyzing natural scene statistics—and therebyascertain, for example, whether two adverts that were viewed by a givenconsumer have something in common beyond their subject matter. Forexample, two adverts may include the same actor and be liked by aconsumer for that reason, even though the products portrayed have littlein common.

In preferred embodiments, the device graph herein is based onprobabilistic data. The probabilistic approach to graph constructionuses behavioral data such as viewership habits to match up users.

In some embodiments, an entity such as a DSP, can construct a devicegraph; in other embodiments it can obtain, such as purchase, it fromanother entity such as a DMP.

In various embodiments herein, both a device graph and a consumer graphare operating together in a manner that permits tying in mobile data toTV data, and therefore facilitates the bidding process on PTV inventory.

The term graph is used herein in its mathematical sense, as a set G(N,E) of nodes (N) and edges (E) connecting pairs of nodes. Graph G is arepresentation of the relationships between the nodes: two nodes thatare connected by an edge are similar to one another according to somecriterion, and the weight of an edge defines the strength of thesimilarity. Pairs of nodes that do not meet the similarity criterion arenot joined by an edge. FIG. 2 illustrates graph concepts, showing 6nodes, N₁-N₆, in which three pairs of nodes are connected by edges.

In the implementation of a graph herein, a node, N, is an entity orobject with a collection of attributes, A. In FIG. 2, each node hasassociated with it an array of attributes, denoted Ai for node Ni.

In the implementation of a graph herein, an edge, E, existing betweentwo nodes indicates the existence of a relationship, or level ofsimilarity, between the two nodes that is above a defined threshold. Theweight of an edge, w_E, is the degree of similarity of the two nodes.The weights of the edges in FIG. 2 are shown diagrammatically asthicknesses (in which case, w_E₁₂>w_E₃₄>w_E₁₅).

In a consumer graph, a node represents an individual, or a householdcomprising two or more individuals, with a set of attributes such as thegender(s) and age(s) of the individual(s), history of TV programswatched, web-sites visited, etc.

FIG. 3 illustrates an exemplary structure of a node of a consumer graph.Each node has a collection of attributes that include types andbehaviors, for which data is continuously collected from first party andthird party sources. Many of the attributes are transmutable if newinformation for the consumer becomes available, and the collection ofattributes (i.e., the number of different attributes stored for a givenconsumer) can also grow over time as new data is collected about theconsumer. An aspect of the technology herein is that the graph isconstructed from a potentially unlimited number of inputs for a givenconsumer, such as online, offline, behavioral, and demographic data.Those inputs are updated over time and allow the data for a givenconsumer to be refined, as well as allow the population of consumers onwhich data can be used to be expanded. The fact that there is no limitto the type and character of data that can be employed means that themethods herein are superior to those employed by panel companies, whichrely on static datasets and fixed populations.

Some of the sources from which data is collected are as follows.

Type data is categorical data about a consumer that normally does notchange, i.e., is immutable. Behavioral data is continuously updatedbased on a consumer's recent activity.

Each node includes a grouping of one or more devices (desktop, mobile,tablets, smart TV). For each device, data on the type of the user basedon the device is collected from third party and first party sources.

Table 1 shows examples of data by category and source.

TABLE 1 1^(st) Party 3^(rd) Party Non-transmutable Census (Govt.)Household income Education level (e.g., from Exelate) Gender (e.g., fromNielsen, DAR) Transmutable Behavior (online) Offline behavior TV viewingRetail Purchases Viewability (e.g., how much of Offsite visits advertseen, kept on, visible (visited pharmacy, online?) movie theater, carOnline sites visited dealership, etc.) Location events

First party data comprises data on a user's behavior, for example:purchases, viewership, site visits, etc., as well as types such asincome, gender, provided directly by a publisher to improve targetingand reporting on their own campaigns. (For example, the Coca Colacompany might provide to a DSP, a list of users who “like” Coke productson social media, to improve their video advertising campaigns.) Firstparty type data can be collected from advertisements served directly tothe device, and from information collected from the device, such as oneor more IP addresses. First party type data includes location from IPaddress, geolocation from mobile devices, and whether the device islocated in a commercial or residential property.

Third party type data is obtained from external vendors. Through aone-on-one cookie synchronization or a device synchronization, anexternal vendor, for example a DMP such as Krux (www.krux.com/),Experian (which provides purchase behavior data), or Adobe, providesinformation about the cookie or device. Example data includes marketsegment occupied by the consumer, such as age range, gender, incomelevel, education level, political affiliation, and preferences such aswhich brands the consumer likes or follows on social media.Additionally, external vendors can provide type data based on recentpurchases attributed to the device. Third party data includesinformation such as gender and income because it is not collecteddirectly from external vendors. Third party data can be collectedwithout serving an advertisement. TV programs viewed and purchases arethird party data.

First Party data is typically generated by a DSP; for example, it isdata that the DSP can collect from serving an Ad or a Brand/Agency thatprovides the data. First party data includes data that depends on havingserved an Ad to have access to it.

Behavioral data can be collected from the devices through first partyand third party sources. Behaviors are first party data typically, andare mutable.

First party behavioral data is collected from advertisements serveddirectly to the device. This includes websites visited, and the TVprogram, or OTT, or video on demand (VOD) content viewed by the device.

Third party behavioral data is obtained from external vendors, typicallyDMP's such as Experian, Krux, Adobe, Nielsen and Comscore, andadvertising exchanges or networks, such as Brightroll, SpotX, FreeWheel,Hulu. Example data includes the history of TV programming viewed on thedevice in the last month, the history of websites visited by a personalcomputer or laptop, or mobile device, and history of location basedevents from mobile devices (for example, whether the device was at aStarbucks). In some instances, the same types of data can be obtainedfrom both first party and third party entities.

Edges between the nodes in the consumer graph signify that the consumershave a threshold similarity, or interact with each other. The edges canbe calculated deterministically, for example, if the nodes are inphysical proximity, or probabilistically based on similarity inattributes. Probabilistic methods utilized include, but are not limitedto: K-means clustering, and connected components analysis (which isbased on graph traversal methods involving constructing a path acrossthe graph, from one vertex to another. Since the attributes aretransmutable, the edges can also change, either in their weighting or bybeing created or abolished if the similarity score for a pair of nodesalters. Thus the graph is not static, and can change over time. In someembodiments, change is dynamic: similarity scores are continuallyrecalculated as nodes attributes for nodes are updated.

Typically, attributes and data are added dynamically (as they areobtained). The graph may be re-constructed weekly to take account of thenew attributes and data, thereby establishing new weightings for theedges, and identifying newly connected or reconnected devices. (Graphconstruction and reconstruction may be done in the cloud, i.e., bydistributing the calculations over many processors on a computernetwork, or on processors warehoused at a datacenter under the controlof the DSP.)

The similarity, S, between two nodes N_1, N_2, is calculated accordingto a similarity metric, which is the inverse of a distance function,f(N_1, N_2): N_1, N_2->S, that defines the similarity of two nodes basedon their attributes.

In a consumer graph, similarity represents the likeness of twoindividuals in terms of their demographic attributes and their viewingpreferences. Similarities can be calculated, attribute by attribute, andthen the individual similarity attributes weighted and combined togetherto produce an overall similarity score for a pair of nodes.

When the attributes of two nodes are represented by binary vectors,there are a number of metrics that can be used to define a similaritybetween a pair of nodes based on that attribute. Any one of thesemetrics is suitable for use with the technology herein. In someembodiments, for efficiency of storage, a binary vector can berepresented as a bit-string, or an array of bit-strings.

When working with a similarity metric that is the inverse of a distancefunction, f(N_i, N_j), a zero value of the distance function signifiesthat the types and behaviors of the two nodes are identical. Conversely,a large value of the distance function signifies that the two nodes aredissimilar. An example of a distance function is Euclidean distance,

f(N_i,N_j)=∥A_i−A_j∥̂2

where A_i, and A_j are the sparse vectors representing the attributes ofnodes N_i and N_j, and the distance is computed as a sum of the squaresof the differences of in the values of corresponding components of eachvector.

Comparisons of binary vectors or bit-strings can be accomplishedaccording to one or more of several similarity metrics, of which themost popular is the Tanimoto coefficient. Other popular metrics include,but are not limited to: Cosine, Dice, Euclidean, Manhattan, city block,Euclidean, Hamming, and Tversky. Another distance metric that can beused is the LDA (latent Dirichlet allocation). Another way of defining adistance comparison is via a deep learning embedding, in which it ispossible to learn the best form of the distance metric instead of fixingit as, e.g., the cosine distance. An example approach is via manifoldlearning.

The cosine dot product is a preferred metric that can be used to definea similarity between the two nodes in a consumer graph. The cosinesimilarity, that is the dot product of A_i and A_j, is given by:

f(N_i,N_j)=A_i·A_j

In this instance, the vectors are each normalized so that theirmagnitudes are 1.0. A value of 1.0 for the cosine similarity metricindicates two nodes that are identical. Conversely, the nearer to 0.0 isthe value of the cosine metric, the more dissimilar are the two nodes.The cosine metric can be converted into a distance-like quantity bysubtracting its value from 1.0:

f′(N_i,N_j)=1−A_i·A_j

An example of a more complex distance function is a parameterizedKernel, such as a radial basis function.

f(N_i,N_j)=exp(∥A_i−A_j∥̂2/ŝ2),

where s is a parameter.

In the more general case in which the bit-string is a vector thatcontains numbers other than 1 and 0 (for example it contains percentagesor non-normalized data), then one can calculate similarity based ondistance metrics between vectors of numbers. Other metrics, such as theMahalanobis distance, may then be applicable.

Typically, a similarity score, S, is a number between 0 and 100, thoughother normalization schemes could be used, such as a number between 0and 1.0, a number between 0 and 10, or a number between 0 and 1,000. Itis also possible that a scoring system could be un-normalized, andsimply be expressed as a number proportional to the calculatedsimilarity between two consumers.

In some embodiments, when calculating a similarity score, eachcontributing factor can be weighted by a coefficient that expresses therelative importance of the factor. For example, a person's gender can begiven a higher weighting than whether they watched a particular TV show.The weightings can be initially set by application of heuristics, andcan ultimately be derived from a statistical analysis of advertisingcampaign efficacy that is continually updated over time. Other methodsof deriving a weighting coefficient used to determine the contributionof a particular attribute to the similarity score include: regression,or feature selection such as least absolute shrinkage and selectionoperator (“LASSO”). Alternatively, it is possible to fit to “groundtruth data”, e.g., login data. In some embodiments, as the system triesdifferent combinations or features, which one leads to greaterprecision/recall can be deduced by using a “held out” test data set(where that feature is not used in construction of the graph).

Another way of deriving a similarity score for a feature is to analyzedata from a successive comparison of advertising campaigns to consumerfeedback using a method selected from: machine learning; neural networksand other multi-layer perceptrons; support vector machines; principalcomponents analysis; Bayesian classifiers; Fisher Discriminants; LinearDiscriminants; Maximum Likelihood Estimation; Least squares estimation;Logistic Regressions; Gaussian Mixture Models; Genetic Algorithms;Simulated Annealing; Decision Trees; Projective Likelihood; k-NearestNeighbor; Function Discriminant Analysis; Predictive Learning via RuleEnsembles; Natural Language Processing, State Machines; Rule Systems;Probabilistic Models; Expectation-Maximization; and Hidden and maximumentropy Markov models. Each of these methods can assess the relevance ofa given attribute of a consumer for purposes of suitability formeasuring effectiveness of an advertising campaign, and provide aquantitative weighting of each.

Representation

To properly assess an entire population of consumers, a large number ofnodes needs to be stored. Additionally, the collection of attributesthat represent a node's types and behaviors can be sizeable. Storing thecollection of the large number of attributes for the nodes ischallenging, since the number of nodes can be as many as hundreds ofmillions. Storing the data efficiently is also important since the graphcomputations can be done most quickly and efficiently if the node datais stored in memory.

In a preferred embodiment, attributes are represented by sparse vectors.In order to accomplish such a representation, the union of all possiblenode attributes for a given type is stored in a dictionary. Then thetype, or behavior, for each node is represented as a binary sparsevector, where 1 and 0 represent the presence and absence of anattribute, respectively. Since the number of possible attributes of agiven type is very large, most of the entries will be 0 for a givenconsumer. Thus it is only necessary to store the addresses of thoseattributes that are non zero, and each sparse vector can be storedefficiently, typically in less than 1/100^(th) of the space that wouldbe occupied by the full vector.

As an example, let the attributes encode the TV programs that a givenconsumer has viewed in the last month. The system enumerates allpossible TV shows in the dictionary, which can be up to 100,000different shows. For each node, whether the consumer watched the show inthe last month is indicated with a 1, and a 0 otherwise.

If the attributes indicate different income levels, multiple incomelevels are enumerated, and a 1 represents that the consumer belongs to aparticular income level (and all other entries are 0).

Thus for a consumer, i, having an annual income in the range$30,000-$60,000, and who has viewed “Top Gear” in the last month, thefollowing is established:

TV_Dictionary={“Walking Dead”, “Game of Thrones”, . . . , “Top Gear”}

TV_i=[0,0, . . . ,1]

TV_i can be stored as simply [4]; only the 4th element of the vector isnon-zero. Similarly, for income:

Income_Dictionary={<$30,000, $30,000-$60,000, $60,000-$100,000,>$100,000}

Income_i=[0,1,0,0]

Income_i can be stored as simply [2], as only the second element of thevector is non-zero.

All the attributes of a node, i, can thus be efficiently representedwith sparse vectors. This requires 2 to 3 orders of magnitude lessmemory than a dense representation.

Graph Construction

FIGS. 4A and 4B illustrate a flow-chart for steps in construction of aconsumer graph.

Initially, the graph is a collection of devices, which are mapped toconsumers. Multiple data sources are used to group multiple devices(tablet, mobile, TV, etc.) to a single consumer. This typically utilizesagglomerative techniques. In order to attribute a single device (e.g., aSmart TV) to multiple consumers, a refinement technique is used.

With agglomerative methods, multiple devices can be grouped to a singleconsumer (or graph node). Some data sources used for this include, butare not limited to:

-   -   IP addresses: multiple devices belonging to same IP address        indicates a single consumer or a household.    -   Geolocation: multiple devices that are nearby, using latitude        and longitude, can be attributed to a single consumer.    -   Publisher logins: if the same consumer is logged in from        multiple devices, those devices can be associated with that        consumer.

During this process, the consumer's identity is masked, to obviateprivacy concerns. The result is a single consumer ID that linksparticular devices together.

Let P(d_i, d_j) be the probability that the two devices, d_i and d_j,belong to the same node (consumer, or household). From multiple datasetsobtained from different categories of device, it is possible toconstruct the probability:

P(d_i,d_j)=w_IP×P(d_i,d_j∥IP)×w_Geo×P(d_i,d_j∥Geo)×w_Login×P(d_i,d_j∥Login)/Z

where “×” means “multiply”, where w_(—) are weighting factors, P(d_i,d_j|Y) is a conditional probability (the probability of observing devicei and device j belong to same user, if Y has the same value for both,and Z is a normalizing factor. Thus, Y may be an IP address. (The valueof the conditional probability may be 0.80). Each data source gets adifferent weighing factor: for example, login data can be weightedhigher than IP addresses. The weights can be fixed, or learned from anindependent validation dataset.

Once multiple devices are grouped to a single node, the Types andBehaviors from the respective devices are aggregated to the singularnode's attributes. For example, attributes (and the corresponding sparsevectors) from mobile (such as location events), and desktop (recentpurchases) are aggregated. This provides more comprehensive informationfor a consumer, permitting more accurate and meaningful inferences for anode to be made.

Associating a device with a given consumer is possible due to the datathat is associated with those devices and known to various mediaconduits. For examples, a Smart-TV stores location information as wellas subscription information about the content broadcast by it. Thisinformation is shared with, and can be obtained from, other entitiessuch as a cable company. Similarly, a mobile device such as a tablet orsmartphone may be associated with the same (in-home) wifi network as theSmart-TV. Information about the location is therefore shared with, e.g.,the cell-phone carrier, as well as broadcasters of subscription contentto the mobile device. A key aspect of the graph methodology herein isthat it permits consumer information to be linked across differentdevice and media platforms that have typically been segregated from oneanother: in particular, the graph herein is able to link consumer datafrom online and offline purchasing and viewing sources with TV viewingdata.

With refinement methods, a single device (for example, a smart TV) canbe associated with multiple consumers (or graph nodes) who, for example,own mobile devices that are connected to the same wifi network as thesmart-TV.

Given a node, n, to which are assigned multiple devices, the variousattributes are clustered into smaller groups of devices, for example, aTV ID, connected to multiple devices from a common IP address. The TVviewership data is aggregated along with the attributes from all thedevices. A clustering algorithm, such as k-means clustering, can beapplied to group the devices into smaller clusters. The number ofclusters, k, can be set generally by the number of devices (by defaultk=# number of devices/4). Sometimes it is possible to only collectaggregate data at a household level. For example, there may be as manyas 20 devices in one household. But by using behavioral data, it can beascertained that the 20 devices have 4 major clusters, say with 5devices each, where the clusters correspond to different individualswithin the same household. Thus, although there are two categories ofdevice (shared and personal), it is still important to attributebehavioral data to users.

Once a shared device is attributed to multiple nodes, the data collectedfrom the device can be attributed to the nodes. For example, TV viewingdata from a Smart TV can be collected from the OEM. Through thisattribution, the TV viewing data can be added to the collection of anode's attributes. Ultimately, a Smart-TV can be attributed to differentpersons in the same household.

Lookalike Modeling by Learning Distance Functions

Given a graph, G(N, E), and a functional form that defines a similaritymetric, and a set of seed nodes, it is possible to generate a set of“lookalike” nodes that are similar to the seed nodes, where similarityis defined by a function that is fixed, or learned. This is useful whenidentifying new consumers who may be interested in the same or similarcontent as a group of consumers already known to an advertiser. Similarprinciples can be utilized when projecting likely viewing behavior ofconsumers from historical data on a population of consumers.

Seed nodes can be a set of nodes, e.g., household(s) or individual(s),from which to generate a set of lookalike nodes using a fixed, orlearned, similarity metric. For example, seed nodes can be defined as anaudience segment (such as list of users that saw a specific show forcertain). This is useful for determining, for each member of theaudience segment, a list of other audience members who might havesimilar viewing habits even if they did not watch exactly the same showas the seeds.

Given the set of seed nodes in a graph (and their attributes), theoutput of lookalike modeling is a set of nodes (that includes the seednodes) that are similar to the seed nodes based on the fixed or learnedsimilarity metric.

Several different vectors can be used in determining look-alike models:One is the vector of TV programs in total. This vector can be as long as40 k elements. Another vector is the list of consumers who saw aparticular program (e.g., The Simpsons). The vector of viewers for agiven TV program can be as long as 10M elements because it contains oneelement per consumer. Another vector would be a vector of web-sitesvisited (say 100 k elements long). Still another vector would be basedon online videos viewed (which can also be 100 k elements long).

In general, TV program comparison data accesses a 10M user base. Onlinedata can identify a potentially much larger audience, such as 150Mconsumers. It should be understood that TV data can be accumulatedacross a variety of TV consumption devices that include, but are notlimited to linear, time-shifted, traditional and programmatic.

The similarity between 2 distinct nodes can be calculated from theirattributes, represented by sparse vectors. Given a distance functionf(N_i, N_j), and a set of seed nodes, N_S, the pairwise distancesbetween each element of the seed nodes, n in N_S, and all other nodesother than the seed node, n′, are calculated. That is, all quantitiesf(n, n′) are calculated.

After calculating all pairwise similarities, only the nodes such thatf(n, n′)<T are selected. T is a threshold maximum distance below whichthe nodes are deemed to be similar. Alternatively, values of f(n, n′)(where n is not n′) are ranked in decreasing order, and the top t nodepairs are selected. In either case, T and t are parameters that arepreset (provided to the method), or learned from ground truth orvalidation data. The set of all nodes n′ that satisfy the criteriaabove, form the set of “lookalike nodes”.

Graph Inference

Given a graph G(N, E), it is also possible to infer likely attributes ofa node, n, based on the attributes of its neighbors in the graph. Thiscan be useful when incomplete information exists for a given consumerbut where enough exists from which inferences can be drawn. For example,TV viewership attributes may be missing for a node n (in general, thereis either positive information if a user did watch a show, or it isunknown whether they watched it), whereas those attributes are availablefor neighbor nodes n′, n″ in the graph. Nodes n, n′, and n″ contain allother attributes, such as income level and websites visited.

In another example, it can be useful to calculate the probability thatthe consumer associated with node n would watch the show “Walking Dead”,given that n′, n″ both also watch “Walking Dead”. If the similarity,given by the weight of the edges between n and n′, n″, are w′, w″=0.8and 0.9 respectively, and the likelihood of n watching the show based onhis/her own attributes is 0.9, then the probability is given by:

$P\left( {n\mspace{14mu} {watches}\mspace{14mu} {``{{Walking}\mspace{14mu} {Dead}}"}} \right)$$\begin{matrix}{= {\left\lbrack {{0.8 \times 0.9} + {0.9 \times 0.9}} \right\rbrack {\text{/}\left\lbrack {{0.8 \times 0.9} + {0.9 \times 0.9} + \left( {1 - {0.8 \times 0.9}} \right) +} \right.}}} \\\left. \left( {1 - {0.9 \times 0.9}} \right) \right\rbrack \\{= 0.765}\end{matrix}$

Similar principles can be utilized when projecting likely viewingbehavior of consumers from historical data on a population of consumers.

Accuracy

The graph is continually refined as new data is received. In oneembodiment, a technique such as machine learning is used to improve thequality of graph over time. This may be done at periodic intervals, forexample at a weekly build stage. It is consistent with the methodsherein that the graph utilized is updated frequently as new consumerdata becomes available.

To determine the accuracy of the graph, the precision and recall can becompared against a validation dataset. The validation dataset istypically a (sub)graph where the device and node relationships are knownwith certainty. For example, the login information from an onlinenetwork such as eHarmony, indicates when the same user has logged intothe site from different desktops (office, laptop), and mobile devices(smartphone and tablet). All the devices that are frequently used tologin to the site are thus tied to the same consumer and thereby thatindividual's graph node. This information can be used to validatewhether the constructed graph ties those devices to the same node.

If D is the set of devices in the validation set, let Z(D) denote thegraph, consisting of a set of nodes, constructed from the set ofdevices, D. For different datasets, and different graph constructionmethods, it is possible to obtain different results for Z(D).

For the set Z(D), true positive (TP), false positive (FP), and falsenegative (FN) rates can all be calculated. True positives are all nodesin Z(D) that are also nodes in the validation set. False positives areall nodes in N(D) that do not belong to the set of nodes in thevalidation set. False negatives are all nodes that belong to thevalidation set, but do not belong to Z(D).

Precision, defined as TP/(TP+FP), is the fraction of retrieved devicesthat are correctly grouped as consumer nodes.

Recall, defined as TP/(TP+FN), is the fraction of the consumer nodesthat are correctly grouped.

Depending on the application at hand, there are different tradeoffsbetween precision and recall. In the case of constructing a consumergraph, it is preferable to obtain both high precision and high recallrates that can be used to compare different consumer graphs.

The validation dataset must not have been used in the construction ofthe graph itself because, by doing so, bias is introduced into theprecision and recall values.

Learning the Similarity Metric:

Another feature of the graph that can be adjusted as more data isintroduced is the underlying similarity metric. Typically, the metric isfixed for long periods of time, say 5-10 iterations of the graph, andthe metric is not reassessed at the same frequency as the accuracy.

In the case where the distance function is not fixed, it is possible tolearn the parameters of a particular distance function, or to choose thebest distance function from a family of such functions. In order tolearn the distance function or its parameters, the values of precisionand recall are compared against a validation set.

Suppose a goal is to predict the lookalike audience segment that arehigh income earners, based on the attributes of a seed set of known highincome earners. The similarity of the seed nodes to all other nodes inthe graph is calculated for different distance functions, or parametersof a particular distance function. The distance function uses theattributes of the nodes, such as online and TV viewership, to calculatethe similarities.

For example, if the distance function is the radial basis function withparameter, s:

f(N_i,N_j)=exp(∥A_i−A_j∥̂2/ŝ2),

then the pairwise distances from the seed nodes to all other nodes, arecalculated for different values of s, using the same threshold distancevalue, T, to generate the set of lookalike nodes. For different valuesof s (the parameter that needs to be learned), the calculations producedifferent sets of lookalike nodes, denoted by N_S(s).

For the set N_S(s), it is possible to calculate true positive (TP),false positive (FP) and false negative (FN) rates. True positives areall nodes in N_S(s) that also belong to the target set in the validationset. In this example, all the nodes that are also high income earners(in ground truth set). False positives are all nodes in N_S(s) that donot belong to the target set (not high income earners). False positivesare all nodes in N_S(s) that do not belong to the target set (not highincome earners). False negatives are all nodes that belong to thevalidation set (are high income earners), but do not belong to N_S(s).

Based on the application, it is possible to require different tradeoffsbetween precision and recall. In the case of targeting an audience withan advertisement, a high recall rate is desired, since the cost ofexposure (an advertisement) is low, whereas the cost of missing a memberof a targeted audience is high.

In the example herein, the aim is to choose the value of s for whichboth the precision and recall rates are high from amongst possiblevalues of s. For other types of distance function, there may be otherparameters for which to try to maximize the precision and recall rates.

The accuracy of a lookalike model can only be defined for a targetaudience segment. For example, it is possible to predict whether alookalike segment also comprises high income earners, from a seed set ofhigh income earners using TV viewing and online behavior datasets.Predictions can be validated using a true set of income levels for thepredicted set of nodes. This gives the accuracy of the predictions.However, the accuracy of predictions for one segment are not meaningfulfor a new target segment, such as whether those same users are alsoluxury car drivers.

Calculating Deduplicated Reach

The consumer graph connects a node (consumer) to all the devices that heor she uses. Thus the graph enables deduplicating the total exposure toan advertisement, to individuals. For example, if user abc123 hasalready seen a particular advertisement on each of his TV, desktop andmobile device, the total deduplicated exposures will count as 1. Thisenables the calculation of the following metrics for direct measurement.

The deduplicated exposed audience is the number of users belonging tothe target audience segment in the consumer graph who were exposed tothe advertisement after deduplication. Then, the direct deduplicatedreach is:

Deduplicated Reach=Deduplicated Exposed Audience/Total Audience

For sampled measurement, this enables the calculation of thededuplicated exposed sampled audience as the number of sampled users whobelong to the target audience segment who were exposed to theadvertisement after deduplication. Then, the sampled reach is:

Deduplicated Sampled Reach=Deduplicated Exposed Sampled Audience/TotalSampled Audience

In the case of modeled measurement data, the ID of the user in theconsumer graph from whom the data was collected is not known. Hence, thereach data cannot be deduplicated on a one-to-one level.

Calculation of deduplicated reach can be useful in managing targeting,if an advertiser wants to impose a frequency cap on consumers (forexample, if the advertiser doesn't want to show the same advert to thesame user more than twice). Deduplicated reach also provides aconvenient metric by which to optimize the efficacy of an advertisingcampaign: for example, by calculating the deduplicated reach over time,as an advertising campaign is adjusted, improvements can continue to bemade by altering parameters of the campaign such as, for example,consumer demographic, or time and channel of broadcast of TV content.

Calculating Incremental Reach

On day t, let the deduplicated reach (direct or sampled) be x. Theincremental reach is the additional deduplicated reach after running thecampaign. In a cross-screen environment, this is a useful parameter tocalculate if an advertiser wants be able to assess whether they canextend a 30% reach via TV to say, a 35% reach by extending to mobileplatforms. One caveat is that in direct measurement of, e.g., TV data,the portion of the sample obtained for smart-TV's is only a subset ofthe overall data, due to the relatively small number of smart-TV'scurrently in the population at large.

In the case of modeled measurement data such as is obtained from a panelwhere the nature of the sample has to be inferred, the ID of the user inthe consumer graph from whom the data was collected is not known. Hence,it is not possible to tell if the same user has viewed the advertisementin the past. Therefore the incremental deduplicated reach cannot becalculated for modeled data because devices cannot be associated withparticular users. Since the incremental reach from the sampledmeasurement, without deduplication, can be calculated, as describedabove, the methods herein are superior to panel-based methods.

Programmatic-TV Bidding

The technology herein provides advertisers with a way to bid on PTVslots, using at least in part, consumer/device graph data that tiesparticular known segments of the consumer population to TV viewers. PTVbidding is different from digital real-time bidding (RTB), which isutilized for online and other digital applications (for example, ashandled by other DSP's), in at least two ways.

A SSP (such as WideOrbit, Videa, Clypd) aggregates TV inventory from aplurality of local TV stations into a common marketplace. DSPs make bidsfor individual or multiple TV spots. Unlike RTB (real-time bidding),which applies to digital inventory and is such that the response time toacknowledge a bid is typically fractions of a second (oftenmilliseconds), the feedback time on a TV bid can be anywhere from asingle day to several weeks. In RTB, e.g., utilizing protocol RTB2.0-2.4, see Internet Advertisers Bureau atwww.iab.com/guidelines/real-time-bidding-rtb-project/, a bid request isbroadcast by a supplier for an immediately available advertisementimpression. Bidders respond with a maximum bid price, based on theparameters of the bid request. There is typically a short time window(less than 50 ms) to receive bids. Once all the bids are accepted, theexchange conducts a second price auction. The winner gets notified, andall others receive a notification of loss. The uncertainty for theadvertiser is over the probability of obtaining a winning bid at a givenprice.

Analog TV bidding is slower than programmatic TV bidding because it isnot real-time and not readily susceptible to algorithmicimplementations. In Programmatic TV bidding, bids are accepted forupcoming spots, up to 14 days in advance, but the bidding may includecontent that is just 1-2 days away from being broadcast.

Additionally, for programmatic TV bidding, the feedback response fromthe content provider can be one of accept, hold, or decline instead ofjust a win/loss response. With a “loss” or “decline” signal, additionalfeedback can be provided so that the bidder can revise and resubmit thebid offer. A machine learning system constructs a probabilitydistribution of win/hold, and loss rates for different bid prices, andparameters that define a spot. The probability distribution to belearned is therefore more complex (and has more dimensions) than in theRTB approach.

This introduces additional complexity that current digital biddingsolutions are not equipped to handle. A bidding architecture and methodfor PTV bidding consistent with the methods and technology herein is asfollows.

In the PTV marketplace, illustrated in FIG. 5, one or more SSPs (SSP1,SSP2, etc.) such as WideOrbit, Videa or Clypd has a programmaticinterface to all the TV supply from TV stations TV1, TV2 . . . TV9, andaggregates that supply. Demand side platforms, DSP1-DSP3, etc., such asentities that can perform the methods herein, have previously only usedreal-time bidder methods (RTB) to make bids on online inventory, andhave accepted that the bidding process on TV inventory takes longer.

According to the methods herein, the decision to bid on an item ofinventory, and the corresponding bid price, are based on expectedperformance of one or more certain key performance indicators (KPIs). Inthe case of PTV inventory buying, the two main categories of KPI are“audience reach” and “direct response.” Other KPI's can be related tothe cost effectiveness of a campaign: for example, the cost per consumerreached (knowing the total cost of advertisement placement) can becalculated and optimized in order to continually refine the campaign.KPI's can inform relevant optimization metrics.

In the category, audience reach, KPIs relate to the exposure of theadvertisement to targeted audience populations, as measured by thereach, or deduplicated reach.

By contrast, direct response KPIs relate to immediate actions taken byconsumers exposed to an advertisement, such as: website visits(expressed as an average number of visits to the advertiser's website byusers after being exposed to the advert); online purchases (purchasesmade in online stores e.g., Amazon.com, by users exposed to theadvertisement); offline purchases (purchases made in physical stores,like a retail store or grocery store, by users exposed to theadvertisement); and location events (the average number of times theusers exposed to the advertisement visited a particular location afterwatching the advertisement).

Bidding on advertising slots based on programmatic TV data depends ontargets set by the advertiser such as a certain minimum GRP, and one ormore optimization metrics; the outcome is a choice of slots, and a bidprice.

Bids are made for a future TV spot (i.e., a slot in a program schedule),and bids can typically be placed as much as two weeks in advance of thatspot being aired. For special events such as sporting events whose dateis known well in advance (e.g., the annual SuperBowl, or the WorldSeries), bids may be placed even further ahead of time. Multiple bidscan be placed for the same spot on separate days as a contingency if thebid on a preferred day was unsuccessful. There are several parametersthat define a spot, including: the program title (e.g., The Simpsons),daypart (a portion of a given day in which the program is broadcast,e.g., Primetime, late night, which might permit differentiation betweenscreening of new content vs. re-runs), and the geographical area inwhich the program is broadcast (e.g., New York designated market area(DMA), or by zip-code).

There are two forms of uncertainty in the bidding and feedback process:whether a bid will be successful, and, if successful, what will be theperformance of the advertisement. This leads to two types of biddingapproaches. In “exploration”, the bidder doesn't have enough data toconclude how valuable a slot may be, and can't say with 100% certaintythat slot is worth a given price, $x. This may be because the slot isassociated with a whole new type of program, or the first season of anew show. In this case, the advertiser must simply submit a bid and seehow it turns out. In “exploitation”, the bidder is using a significantbody of data that has already been generated. A unique aspect of thistype of bidding is that there are tradeoffs between the two types ofapproach.

There are three possible outcomes of a bid: Win (success; the bid offerhas been accepted by seller); loss (failure; the bid offer was declinedby seller); and “hold” (the seller has accepted the bid as part of ablock or “rotation” of spots/inventory). If a bid is successful, the adis broadcast to all of the TVs in the targeted demographic.

A “hold” is an intermediate outcome for an advertiser. For example, ifthe advertiser bid on a particular 2 of 8 offered spots between 9 and 12pm, and the seller commits to playing the ads in 2 of the 8 spots butwithout specifying which of the particular spots, that is a “hold”.Another type of hold is where an advertiser specified a number of slots;the didn't win top choice but were provided an alternate set of slots ora subset of the slots bid on, not including the most preferred slot. Insuch instances, the advertiser might make a second bid to get theremaining slots, or make another bid on other similar slots.

Additional feedback can accompany a loss outcome, such as a possibilityof revising and resubmitting the bid, but in general, more informativeinformation to the advertiser is gleaned from a “hold” outcome.

This is a unique aspect of bidding on programmatic TV content. If a bidprice wins, it maybe because the price was too high and the advertiserwill not know how much lower it could have bid and still have beensuccessful. By contrast, a hold means in practice that the spot islocked and pooled together with other spots of a similar character. Forexample, there may be 10 slots available, and an advertiser bids on twoof them. The two spots for which a “hold” is returned will be clearedwhen the whole block clears. That gives an advertiser a better idea ofwhat price they can bid and be confident that they will not loseoutright. For this reason, a hold outcome has more information (and thusa greater reduction in uncertainty) for an advertiser than a winoutcome, since the advertiser can infer the clearing price for an entireblock of spots when the response is a hold. Thus in programmatic TVbidding, a good heuristic is to aim for a Hold outcome, rather than aWin outcome, a factor that differentiates it from the digital RTB case.Conversely, if a bid is initially ‘successful’ (a ‘win’), the outcomeguaranteed. In other words, the highest bidder cannot be outbid insubsequent rounds of bidding.

In the bidding process, therefore, there is uncertainty over whether abid price for a specific spot (as defined by program title, daypart,DMA, etc.) will lead to a win, loss, or hold. It is possible toconstruct a probability distribution, H, of the outcome of a bid (win,loss or hold) for parameters such as descriptions of the inventory (suchas daypart, TV program) and/or audience demographics (DMA), etc. In theformulae, the parameters are collectively given by Theta, θ, for a givenbid price (P):

Π(Outcome=Win/Loss/Hold|θ,P)

As a DSP obtains more sample data on outcomes at different bid pricesfor specific parameters, the less the uncertainty over the outcome. Theprobability distribution, H, can be refined with Bayesian updating afterobserving each new data point.

The other form of uncertainty is how well the advert will perform in thegiven TV spot, as measured by audience reach or direct response KPI's.After the advertisement is served on a spot, by measuring the reach(such as GRPs) or direct response (like website visits), some aspect ofperformance can be quantified. Since the performance of a spot is notrepeatable, (it can vary with time), the uncertainty of the spot'sperformance can be denoted by, for example: Π(KPI=50 GRPs|θ). Such aformula represents the probability of hitting a given KPI (such as 50)for a particular type of slot described by parameters θ.

Bayesian updating can be used to depict the uncertainty, which declinesas more data points are observed.

When bidding on a TV spot, an advertiser wants to set a bid price sothat it can achieve a win or hold outcome, as well as to bid on specificspots at a specific price to achieve an expected performance (anaudience reach or direct response KPI). It is possible to identify aspot with low uncertainty on expected performance at a price that has ahigh probability of win/hold. This is the case for spots where a lot ofdata is available, and is the “exploit” scenario, as describedhereinabove. Alternatively, it is possible to pick a spot and a pricewhere there is very little or no sampled data; by finding a spot thathas a high performance and leads to a win or a hold at low bid prices,it is possible to greatly reduce the uncertainty of the unknown spot.This is the “explore” scenario, as described hereinabove.

The ability to base bids on a growing body of information aboutpreviously successful (and unsuccessful) bids allows the overall biddingprocess to be more efficient for a given advertiser. FIG. 6 illustratesthis. From a lot of samples, suppose it is known that an advertiser canobtain a certain level of GRP's at a particular bid price ($22 in thisexample) and at particular values of a set of other variables. That bidprice represents a location in multi-factor space, represented by thedark box in FIG. 6. In this example, for convenience of representation,there are three variables (shown on orthogonal axes as location,daypart, and program name), though in practice there may be more than 3.The gray boxes represent information (feedback values, or outcomeprobabilities) about less successful bids: if the advertiser can deviatefrom the optimal bid, the outcome will be close and the advertisementmay give rise to a similar type of performance. This knowledge leads toreduced uncertainty in the outcome of a bid, and is based on aniterative learning process. The darker the box in the grid, the morecertain the outcome. The values for the other boxes allow an advertiserto make inferences on the values of similar blocks of inventory, basedon having similar parameters, such as same daypart or same geographiclocation.

The objective of bidding is to maximize the performance metric (audiencereach, or direct response KPIs) at a given price, or to achieve a levelof performance for the lowest cost. Since the performance is uncertain,another spot for which data is not available could have performed betterat a lower cost. Thus in the long term, for the bidder, there is valuein data collection by exploring spots with high uncertainty. Thisprinciple is captured by an exploration bonus parameter, U. The generalform of the objective function is given by equation (1):

Value[spot]=ExpectedValue[spot]+U*Reduction in Uncertainty[spot]  (1)

The expected value of the spot is obtained from the expected performanceby integrating over the uncertainty values. Thus, an exemplarydefinition of the expected value is given by equation (2):

ExpectedValue[spot]=Σ_(θ) P(KPI=×|θ)σ(KPI|θ)  (2)

Here σ(KPI|θ) is the uncertainty over the value that KPI will equal xfor a given θ. The sum is taken over all values of θ.

The reduction in uncertainty of the spot can be given by criteria suchas the expected reduction in entropy, or value of information, orinformation gain, according to formulae standard in the art.

An exemplary definition of change in uncertainty is given by equation(3):

ΔUncertainty[θ]=σ(KPI|θ)−Σ_(x)σ(KPI=×|θ)  (3)

where the sum is taken over all discretized values of the KPI.

The foregoing formula, when applied in conjunction with good data, leadto advantages in the bidding process.

Programmatic TV Bidding Method

An exemplary method of bidding on a Programmatic TV slot, can beexpressed as follows. First, an advertiser sets a target budget, B. Inthe following, let S denote the set of all available spots.

-   -   i. Enumerate all sets s in S.    -   ii. Compute the value of set s using Equation (1).    -   iii. Assign a bid price P(s) to each s, using a prior        distribution P(Outcome=Win/Loss/Hold|s, P(s)).

The prior distribution can be estimated from rate card data, for exampleones provided by a company such as SQAD (Tarrytown, N.Y.; internet atsqad.com). Certain data providers specialize in information for TVbuying: based on relationships with sources of TV inventory spots, theysupply price ranges for advertising slots.

-   -   iv. Enumerate all (non-repeating) combinations of s, and denote        each combination by θ.    -   v. Calculate a Score by summing over all values of s in θ.    -   vi. Score[θ]=Σ_(s)P(s) P(Outcome=Win/Hold|s, P(s)) P(KPI=x|s)    -   vii. Choose the θ with the highest score such that the expected        target budget Σ_(s) P(s) P(Outcome=Win/Hold|s, P(s))<=B.

The spots to bid on are the ones in the combination θ, that maximize thevalue of the Score, and whose expected target budget is less than B.

The expected target budget is calculated by taking into account theprobability that the bid price P(s) will have an outcome of Win or Hold.Essentially, if the average probability of a Win or Hold outcome isexpected to be 0.10, then by bidding on spots with a total budget of10B, the advertiser expects to spend a budget of B.

Modeling of User Device Habits

Solving the issue of disparate data tracking requirements involvesprocessing data inputs that may have sequencing tags that reflect thenature of each category of device or medium. On one embodiment, thetechnology herein addresses this issue by allocating data based on thenature of each device. For example, a first batch of consumer data islimited to data mapped from the specific devices that a specificconsumer uses, the sources being a plurality of third party APIs. Thisdevice data updates such aspects as when, where, how, how long, and thelevel of engagement by the consumer, on each particular device. The datais then integrated and processed separately from other consumer data.All device usage data is integrated to create a more preciseunderstanding of the access points and behavior of the consumer overtime.

In another example, a batch of consumer data is based on consumptiondata. Within the data stored for each device, exists the potential toinclude a further plurality of third party data concerning actual mediaconsumed. This data can be obtained from content providers, OEMs,publishers, other data aggregators, and measurement providers (such asNielsen). This data provides information as to what content the consumerhas watched. By understanding what a consumer has watched, it ispossible to understand the consumer's taste and preferences, understandwhat TV shows they are watching, as well as when, where and on whatdevice they are watching them. There are various deterministic methods(for example, understanding which member of the family has logged intotheir Netflix account) to determine which individual in a household isviewing which content.

With such a structure, the system is capable of integrating andprocessing data within different categories as well as across differentcategories. One example of this is comparing a complete set of user datafor a given consumer to complete sets of data on other members of themarket segment. Each complete user dataset is cross-compared to everyother user dataset. The system then matches like behaviors, and is ableto determine granular differences which may affect advertisingperformance. Such determinations then also fine tune the predictivealgorithm on a consumer-by-consumer basis.

Setting Up an Advertising Campaign

An advertiser selects various campaign parameters and campaign goals.The advertiser can select particular parameters such as a demographic,and then values for a percentage of the overall population that meetsthe criteria of the campaign. For example, the advertiser can targetwomen located in California between the ages of 20 and 30 years old, andspecify a reach of 20% of that segment of the population. Anothercriterion could be the frequency by which the advertisement reaches aparticular demographic, such as “two impressions for each age group”.Criteria can be narrowed to identify users who are known to be in themarket for a particular product, e.g., targeting women who have recentlysearched for a Nike® shoe.

Taken together, the advertiser-specified criteria can be ranked andweighted by importance. For example, women from San Francisco could beweighted more highly than women located in Sacramento; in which case,the system can allocate and budget impressions to those particularsubsets of the overall demographic with higher weights. Additionally,special classifications can be placed on viewers based on their productpurchase patterns and media consumption. Assumptions can be built intothe campaign parameters based on purchase history, such that individualswho have purchased luxury handbags are more inclined to respondfavorably to luxury handbag advertisements.

Next, the system makes it possible to run the various campaignparameters and goals against consumer data from first party databases,and received via third party API's.

The underlying consumer data inputs are integrated from a plurality ofsources, which include internally developed datasets produced via amachine learning process, as well as third party data sources. Forexample, third party API's representing consumer and viewer data can becombined with data that exists as a result of internally processed datastreaming from a series of processes that, for example, track viewerbehavior and can predict viewer outcomes. Purchasing suggestions areprovided, and comparisons can be derived based on the application ofrelevant, real-time metrics. The system can further assist in theexecution of deal bidding and buying.

In a preferred embodiment, bidding and buying of programmatic TVadvertising inventory is achieved at a much faster rate, and with a morereliable outcome, compared to existing methods due to the use ofintegrated data from a number of different sources, and the biddingmethods described herein. This improvement also provides significantlybetter accuracy in predicting data models around media consumption andconsumer behavior. For example, the system is able to incorporate arange of data related to other buyers on the exchange, so that purchasesare optimized based on considerations such as the distribution ofinventory.

In a preferred embodiment, the system incorporates APIs from thirdparties who track relevant metrics such as consumer behavior andconsumer demographics (for example, age, race, location, and gender).Relevant metrics are analyzed against the buyer's campaign requirements,such as budget, desired audience, and number of impressions.

The system intakes programmatic TV inventory data via APIs from TVnetworks, distributors such as cable companies, and content providers.In some embodiments of the system and methods herein, data concerninginventory is aggregated across different mediums, so that inventoryavailable for digital, mobile, TV (linear and programmatic), and OTT maybe combined, thereby allowing advertisers to allocate their budgetacross a variety of mediums, device categories and content channels.

Once the advertiser has assigned campaign parameters, and the system hasidentified the possible inventories, the system permits the advertiserto choose a strategy for optimizing the allocation of impressions. Thereare various advertising strategies available to the advertiser.

Exemplary factors for advertising strategies are as follows:

-   -   Pacing: a rate at which an advertiser runs their advertising;    -   Uniform pacing: allocated evenly based on budget and length of        campaign;    -   Accelerated buying: buying based on performance (e.g., the        system detects a whole population of car video watchers and        autonomously allocates based on that discovery)    -   Competitive pacing: if an advertiser's competitor is heavily        buying a particular slot, the advertiser can choose whether to        compete with them, or to allocate away from those slots (this is        applicable to any device medium)    -   Specified time-frame strategies: advertising is purchased based        on the time of day, and day of the week. End-user advertisers        can buy inventories across the entire day, e.g., run every six        hours, or limit the purchasers to only specified times during        the day.    -   Inventory strategy: advertising is purchased based on maximizing        performance of advertising dollars spent or meeting specified        campaign parameters and goals.    -   Pricing strategy: purchasing focuses on staying within a defined        budget. Budgets can be allocated by dollar amount according to        inventory, medium, and/or time-frame.    -   Medium strategy: system detects which medium performs best to        meet campaign goals.

Segmented media planning is the practice of deploying a number of mediastrategies to different inventory types. Existing strategies fold manyconsumers into high-level consumer classifications. The presenttechnology, however, is directed towards user-specific and devicespecific matching of inventory. In a preferred embodiment of the presentinvention, there is a one-to-one audience match to inventory. In otherwords, the advertiser can specify that a particular item of advertisingcontent, such as online content, is directed to a particular individual.Furthermore, the one-to-one inventory to consumer match can be furtherdelineated to a particular user-device such as a TV or mobile handset.

In one example of this process, an advertiser will set their mediastrategy directed towards a demographic defined as middle-aged women.The system can then segment the population of middle-aged women intovarious sub-segments, such as women with children, and women withoutchildren. Next, the system can match an advertising strategy, such as auniform pacing strategy, to the individual level, say, a particularwoman with children that has searched for diapers in the past hour. Thesystem can then allocate the advertisement to a specific device of thewoman, such as her mobile phone so that the next time she opens up anapplication, such as YouTube, the advert will display.

Delivering and Optimizing Cross-Screen Advertising Content

The technology described herein permits an advertiser to targetadvertising content to a consumer across more than one media conduit,including both TV and online media. In particular embodiments theadvertiser is able to specifically bid on programmatic TV content usingbidding methods as described elsewhere herein. There are two types ofenvironment in which an advertiser can target a consumer. In a 1:1environment, a DSP can just use the actual segment and/or a modeled outversion of the actual segment, to make a real time decision to place theadvert if the consumer matches the targeting parameters. In an indexapproach, when it is not possible to target 1:1 and it is not possibleto do dynamic advert insertion or real time decisioning, the systeminstead looks at concentration of viewers projected to access the slot(such as a TV program or VOD program) and then targets the slots thathave the highest concentration of the target consumers.

In a preferred embodiment, the advertiser has control over theallocation of the advertising content because the advertiser accessesthe system via a unified interface that presents information aboutinventory, manages bids on the inventory, and provides a list ofpotential advertising targets consistent with a campaign description andthe advertiser's budget. The system then communicates with, for example,supply-side providers and TV content distributors to ensure that thedesired slots are purchased, typically via a bidding process, and theadvertising content is delivered or caused to be delivered.

In one embodiment, the technology provides for an advertising campaignthat involves delivery of content across two or more media conduits, aswell as delivery of a single advertisement to multiple consumers atdifferent times on, say, TV. The system thereby permits delivery ofadvertising content to a given consumer, or a population of consumers,on more than one device. For example, a consumer may view a portion ofthe campaign on a TV, and may also see the campaign within a desktopbrowser session on their laptop or on their OTT device. In this context,the TV inventory can be purchased across a variety of TV consumptiondevices that include, but are not limited to linear, time-shifted,traditional and programmatic TV, according to bid methodology describedherein or familiar to those skilled in the art. In some instances, theadvertiser desires to cap the number of impressions that a givenconsumer receives; in other instances, the advertiser wants to extendthe campaign from one media to another based on metrics calculatedacross various media conduits. The method permits the advertiser totarget segments of a population more precisely than before, as well asachieve fine scale refinement of a campaign based on performanceindicators from more than one conduit.

There are two aspects of the technology that enable an advertiser tosuccessfully manage and refine and advertising campaign: the system isable to keep track of which devices a given consumer can access, as wellas on which devices the user has already been exposed to the advertisingcampaign; the system can also identify those consumers that are mostlikely to be interested in the campaign's content. Accuracy in targetingcan thus be achieved via predictions based on a mapping of consumerbehavior from aggregated cross-screen viewership data. Such informationcan be used continually when bidding on Programmatic TV, so that bidsare refined and updated at a much faster frequency than would have beenpossible for linear TV.

The analytics portion of the system is capable of accepting unlimiteddata inputs regarding consumer behavior across various media. The systemuses this data to optimize consumer classifications. A second part ofthe output is improved measurement and prediction of future consumerbehaviors based on the data on cross-screen behavior.

Analysis of cross-screen data is able to determine where and when aconsumer has viewed an advertisement, or a particular version of it, andthereby permits advertisers to schedule broadcast of an advertisingcampaign across multiple platforms. Advertisers can then schedule where,when, and how an advertisement is subsequently broadcast. They havecontrol over retargeting (whether they show the same advertising morethan once), or can choose to broadcast a multi-chapter advertisingstory.

One method for managing delivery of advertising content on TV to apopulation of consumers illustrated in FIG. 8. A consumer graph is, orhas been, constructed 710, or is continually under construction andrevision, according to methods described elsewhere herein, and a pool ofconsumers is defined 730, based on the graph of consumer properties,wherein the graph contains information about the devices used by eachconsumer and demographic data on each consumer, and wherein the pool ofconsumers contains consumers having at least a threshold similarity to amember of a target audience.

The system receives a list of advertising inventory 712 from one or moremedia conduits or TV content providers, wherein the list of inventorycomprises one or more slots for TV.

The system receives a pricepoint 702 one or more campaign descriptions705 from an advertiser, wherein each of the campaign descriptions 705comprises a schedule for delivery of or more items of advertisingcontent across two or more devices accessed by a consumer, and a targetaudience 720, wherein the target audience is defined by one or moredemographic factors selected from factors such as: age range, gender,income, and location. Pricepoint 702 represents an advertiser's budgetfor the portion of the advertising campaign on TV. The budget can beallocated across multiple slots, according to the inventory and goalsfor the campaign. Goals may include the target audience desired to bereached, and the hoped for number of impressions.

Based on the pool of consumers, the campaign descriptions and availableinventory, the system can identify one or more advertising targets,wherein each of the one or more advertising targets comprises one ormore TV slots that are to be delivered within TV content identified aslikely to be viewed by the pool of consumers, consistent with a givenpricepoint 702 associated with a campaign description 705. (Theassessment of likelihood of viewing by the pool is derived from theconsumer graph (which allows the system to look at the pool ofconsumers' online views, and the TV data watched by given viewers andthen to tie the online viewing to the TV viewing likelihood based on thecombined information.) It is then possible to bid on 750 the one or moreadvertising targets based on the inventory. In the case of a successfulbid, the content provider is instructed to deliver 770 the ad to theconsumer consistent with the slots. In the case of a “hold”, the bidclears along with other inventory.

The foregoing steps may be carried out in orders other than as describedabove, or iteratively, sequentially, or simultaneously, in part. Thus,the system may receive the campaign descriptions and pricepoint(s), atthe same time as it receives advertising inventory, or beforehand, orafterwards. The consumer graph, additionally, may be continually beingupdated.

For a given consumer, a number of devices accessed by that consumer areidentified 740. This can be from constructing a device graph asdiscussed elsewhere herein. Those consumers for which more than onedevice has been associated, can be targeted by an advertising campaignherein. This information can be used in refining the consumer pool.

Inputs to the system of the various categories of data (inventory,advertising campaigns, etc.) can be via various application programinterfaces (APIs), the development of which is within the capability ofone skilled in the art.

It is to be understood that the instructing and delivery steps areoptional for a given entity performing the method, once the slots of TVinventory have been identified as consistent with the advertisingcampaign.

The methods herein can also be used to optimize an advertising campaignacross a plurality of devices accessible to a consumer. Such methodsbuild upon methods of delivery described hereinabove and with respect toFIG. 7. Once it has been determined that a consumer is a member of atarget audience, and a first and second device accessible to theconsumer have been identified, an advertiser wants to purchase of slotsfor a first and second item of advertising content on the first andsecond devices, consistent with an advertising budget and the targetaudience, in a manner that improves upon prior campaigns.

In this instance, the system can receive feedback on a consumer'sresponse to the first and second items of advertising content, and basedon that information as well as similar information from other consumers,it is possible to use the feedback to instruct purchase of further slotsfor the first and second items of advertising content.

For example, the system can receive a first datum from a first tag thataccompanied the first item of advertising content to validate whether aparticular consumer viewed the first item of advertising content on thefirst device as well as a second datum from a second tag thataccompanied the second item of advertising content. A given datum ofcontent can be a beacon, such as communicated via a protocol such asVPAID or VAST.

In some embodiments, a datum can comprise a confirmation of whether theconsumer has seen the first item of advertising content, in which casethe second item of advertising content is not delivered to the consumeruntil the consumer has seen the first item of advertising content.

In some embodiments, the advertising campaign can be optimized in anumber of different ways. Although measurements of deduplicated reach,as described elsewhere herein, can be used to assess—and refine—theeffectiveness of an advertising campaign, another factor is the overallcost effectiveness of the campaign. For example, given a budget, or adollar-amount spent per advertisement, the cost per impression can becalculated. This number can be optimized over successive iterations ofthe campaign.

In other embodiments, an advertising campaign is updated and optimizedduring its own term. For example, a campaign may be scheduled to runover a particular time period, such as 3 days, 1 week, 2 weeks, 1 month,or 3 months. The system herein can provide feedback on the efficacy ofthe campaign before it is complete, and can therefore provide anadvertiser with an ability and opportunity to adjust parameters of thecampaign in order to improve its reach. Such parameters include, but arenot limited to, aspects of audience demographic such as age, income,location, and media on which the advertisement is delivered, such as TVstation, or time of day.

The system and methods herein can still further provide an advertiserwith a way to project viewer data accumulated historically on to futurepotential viewing habits, for example, using look-alike modelling. Thehistorical data can include data acquired during the course of thecampaign.

Computational Implementation

The computer functions for manipulations of advertising campaign data,advertising inventory, consumer and device graphs in representationssuch as bit-strings, and bidding methods on programmatic TV content, canbe developed by a programmer or a team of programmers skilled in theart. The functions can be implemented in a number and variety ofprogramming languages, including, in some cases mixed implementations.For example, the functions as well as scripting functions can beprogrammed in functional programming languages such as: Scala, Golang,and R. Other programming languages may be used for portions of theimplementation, such as Prolog, Pascal, C, C++, Java, Python,VisualBasic, Perl, .Net languages such as C#, and other equivalentlanguages not listed herein. The capability of the technology is notlimited by or dependent on the underlying programming language used forimplementation or control of access to the basic functions.Alternatively, the functionality could be implemented from higher levelfunctions such as tool-kits that rely on previously developed functionsfor manipulating mathematical expressions such as bit-strings and sparsevectors.

The technology herein can be developed to run with any of the well-knowncomputer operating systems in use today, as well as others, not listedherein. Those operating systems include, but are not limited to: Windows(including variants such as Windows XP, Windows95, Windows2000, WindowsVista, Windows 7, and Windows 8, Windows Mobile, and Windows 10, andintermediate updates thereof, available from Microsoft Corporation);Apple iOS (including variants such as iO53, iO54, and iO55, iO56, iO57,iO58, and iO59, and intervening updates to the same); Apple Macoperating systems such as OS9, OS 10.x (including variants known as“Leopard”, “Snow Leopard”, “Mountain Lion”, and “Lion”; the UNIXoperating system (e.g., Berkeley Standard version); Android operatingsystems; and the Linux operating system (e.g., available from numerousdistributors of free or “open source” software).

To the extent that a given implementation relies on other softwarecomponents, already implemented, such as functions for manipulatingsparse vectors, and functions for calculating similarity metrics ofvectors, those functions can be assumed to be accessible to a programmerof skill in the art.

Furthermore, it is to be understood that the executable instructionsthat cause a suitably-programmed computer to execute the methodsdescribed herein, can be stored and delivered in any suitablecomputer-readable format. This can include, but is not limited to, aportable readable drive, such as a large capacity “hard-drive”, or a“pen-drive”, such as connects to a computer's USB port, an internaldrive to a computer, and a CD-Rom or an optical disk. It is further tobe understood that while the executable instructions can be stored on aportable computer-readable medium and delivered in such tangible form toa purchaser or user, the executable instructions can also be downloadedfrom a remote location to the user's computer, such as via an Internetconnection which itself may rely in part on a wireless technology suchas WiFi. Such an aspect of the technology does not imply that theexecutable instructions take the form of a signal or other non-tangibleembodiment. The executable instructions may also be executed as part ofa “virtual machine” implementation.

The technology herein maybe accessed via a web-browser interface but isnot limited to a particular web browser version or type; it can beenvisaged that the technology can be practiced with one or more of:Safari, Internet Explorer, Edge, FireFox, Chrome, or Opera, and anyversion thereof.

Computing Apparatus

An exemplary general-purpose computing apparatus 900 suitable forpracticing the methods described herein is depicted schematically inFIG. 9.

The computer system 900 comprises at least one data processing unit(CPU) 922, a memory 938, which will typically include both high speedrandom access memory as well as non-volatile memory (such as one or moremagnetic disk drives), a user interface 924, one more disks 934, and atleast one network or other communication interface connection 936 forcommunicating with other computers over a network, including theInternet, as well as other devices, such as via a high speed networkingcable, or a wireless connection. There may optionally be a firewall 952between the computer and the Internet. At least the CPU 922, memory 938,user interface 924, disk 934 and network interface 936, communicate withone another via at least one communication bus 933.

CPU 922 may optionally include a vector processor, optimized formanipulating large vectors of data.

Memory 938 stores procedures and data, typically including some or allof: an operating system 940 for providing basic system services; one ormore application programs, such as a parser routine 950, and a compiler(not shown in FIG. 9), a file system 942, one or more databases 944 thatstore advertising inventory 946, campaign descriptions 948, and otherinformation, and optionally a floating point coprocessor where necessaryfor carrying out high level mathematical operations. The methods of thepresent invention may also draw upon functions contained in one or moredynamically linked libraries, not shown in FIG. 9, but stored either inmemory 938, or on disk 934.

The database and other routines shown in FIG. 9 as stored in memory 938may instead, optionally, be stored on disk 934 where the amount of datain the database is too great to be efficiently stored in memory 938. Thedatabase may also instead, or in part, be stored on one or more remotecomputers that communicate with computer system 900 through networkinterface 936.

Memory 938 is encoded with instructions for receiving input from one ormore advertisers and for calculating a similarity score for consumersagainst one another. Instructions further include programmedinstructions for performing one or more of parsing, calculating ametric, and various statistical analyses. In some embodiments, thesparse vector themselves are not calculated on the computer 900 but areperformed on a different computer and, e.g., transferred via networkinterface 936 to computer 900.

Various implementations of the technology herein can be contemplated,particularly as performed on computing apparatuses of varyingcomplexity, including, without limitation, workstations, PC's, laptops,notebooks, tablets, netbooks, and other mobile computing devices,including cell-phones, mobile phones, wearable devices, and personaldigital assistants. The computing devices can have suitably configuredprocessors, including, without limitation, graphics processors, vectorprocessors, and math coprocessors, for running software that carries outthe methods herein. In addition, certain computing functions aretypically distributed across more than one computer so that, forexample, one computer accepts input and instructions, and a second oradditional computers receive the instructions via a network connectionand carry out the processing at a remote location, and optionallycommunicate results or output back to the first computer.

Control of the computing apparatuses can be via a user interface 924,which may comprise a display, mouse 926, keyboard 930, and/or otheritems not shown in FIG. 9, such as a track-pad, track-ball,touch-screen, stylus, speech-recognition, gesture-recognitiontechnology, or other input such as based on a user's eye-movement, orany subcombination or combination of inputs thereof. Additionally,implementations are configured that permit a purchaser of advertisinginventory to access computer 900 remotely, over a network connection,and to view inventory via an interface having attributes comparable to924.

In one embodiment, the computing apparatus can be configured to restrictuser access, such as by scanning a QR-code, gesture recognition,biometric data input, or password input.

The manner of operation of the technology, when reduced to an embodimentas one or more software modules, functions, or subroutines, can be in abatch-mode—as on a stored database of inventory and consumer data,processed in batches, or by interaction with a user who inputs specificinstructions for a single advertising campaign.

The results of matching advertising inventory to criteria for anadvertising campaign and for bidding on programmatic TV slots, ascreated by the technology herein, can be displayed in tangible form,such as on one or more computer displays, such as a monitor, laptopdisplay, or the screen of a tablet, notebook, netbook, or cellularphone. The results can further be printed to paper form, stored aselectronic files in a format for saving on a computer-readable medium orfor transferring or sharing between computers, or projected onto ascreen of an auditorium such as during a presentation.

ToolKit: The technology herein can be implemented in a manner that givesa user (such as a purchaser of advertising inventory) access to, andcontrol over, basic functions that provide key elements of advertisingcampaign management. Certain default settings can be built in to acomputer-implementation, but the user can be given as much choice aspossible over the features that are used in assigning inventory, therebypermitting a user to remove certain features from consideration oradjust their weightings, as applicable.

The toolkit can be operated via scripting tools, as well as or insteadof a graphical user interface that offers touch-screen selection, and/ormenu pull-downs, as applicable to the sophistication of the user. Themanner of access to the underlying tools by a user is not in any way alimitation on the technology's novelty, inventiveness, or utility.

Accordingly, the methods herein may be implemented on or across one ormore computing apparatuses having processors configured to execute themethods, and encoded as executable instructions in computer readablemedia.

For example, the technology herein includes computer readable mediaencoded with instructions for executing a method for having at least oneprocessor configured to execute instructions for implementing a methodfor targeting delivery of advertising content to a population ofconsumers across one or more slots in television programming, theinstructions including instructions for: receiving a pricepoint and oneor more campaign descriptions from an advertiser, wherein each of thecampaign descriptions comprises a schedule for delivery of an item ofadvertising content across one or more televisions accessed by aconsumer in the population of consumers, and a target audience, whereinthe target audience is defined by one or more demographic factors;defining a pool of consumers based on a graph of consumer properties,wherein the graph contains information about two or more TV and mobiledevices used by each consumer, demographic and online behavioral data oneach consumer, and similarities between pairs of consumers, and whereinthe pool of consumers comprises consumers having at least a thresholdsimilarity to a member of the target audience; receiving a list ofinventory from one or more content providers, wherein the list ofinventory comprises one or more TV slots; identifying one or moreadvertising targets, wherein each of the one or more advertising targetscomprises one or more TV slots that are to be delivered within TVcontent identified as likely to be viewed by the pool of consumers,consistent with one or more of the campaign descriptions, and an overallcost consistent with the pricepoint; bidding on one or more of the TVslots in an advertising target; and if the bidding results in a successor a hold, instructing a media conduit to deliver the item ofadvertising content within the one or more TV slots to a consumer in thepopulation of consumers on a television.

Correspondingly, the technology herein also includes a computingapparatus having at least one processor configured to executeinstructions for implementing a method for executing a method fortargeting delivery of advertising content to a population of consumersacross one or more slots in television programming, the instructionsincluding instructions for receiving a pricepoint and one or morecampaign descriptions from an advertiser, wherein each of the campaigndescriptions comprises a schedule for delivery of an item of advertisingcontent across one or more televisions accessed by a consumer in thepopulation of consumers, and a target audience, wherein the targetaudience is defined by one or more demographic factors; defining a poolof consumers based on a graph of consumer properties, wherein the graphcontains information about two or more TV and mobile devices used byeach consumer, demographic and online behavioral data on each consumer,and similarities between pairs of consumers, and wherein the pool ofconsumers comprises consumers having at least a threshold similarity toa member of the target audience; receiving a list of inventory from oneor more content providers, wherein the list of inventory comprises oneor more TV slots; identifying one or more advertising targets, whereineach of the one or more advertising targets comprises one or more TVslots that are to be delivered within TV content identified as likely tobe viewed by the pool of consumers, consistent with one or more of thecampaign descriptions, and an overall cost consistent with thepricepoint; bidding on one or more of the TV slots in an advertisingtarget; and if the bidding results in a success or a hold, instructing amedia conduit to deliver the item of advertising content within the oneor more TV slots to a consumer in the population of consumers on atelevision.

Cloud Computing

The methods herein can be implemented to run in the “cloud.” Thus theprocesses that one or more computer processors execute to carry out thecomputer-based methods herein do not need to be carried out by a singlecomputing machine or apparatus. Processes and calculations can bedistributed amongst multiple processors in one or more datacenters thatare physically situated in different locations from one another. Data isexchanged with the various processors using network connections such asthe Internet. Preferably, security protocols such as encryption areutilized to minimize the possibility that consumer data can becompromised. Calculations that are performed across one or morelocations remote from an entity such as a DSP include calculation ofconsumer and device graphs, and updates to the same.

Examples Example 1: Increasing PTV Inventory Win-Rate, and UserInterface for Same

A first use case is to help advertisers buy TV inventory. In recentyears, advertisers have been able to buy TV inventory programmatically,where inventory is auctioned off in an automated marketplace. In theseauctions, buyers may have several attempts to bid on an item and receivewin/loss notifications about each bid but no other information regardingthe bid price of other buyers. This contrasts with traditional TVadvertisement buying, where TV inventory is bought and sold through aprocess of negotiations between the sellers and advertisers.

In this use case, users would define an audience they wish to target,e.g., Males 25-34, select inventory from the auction they wish to buyand then submit it to an automated bidding system. In one exemplaryimplementation, the user can do this via a user interface shown in FIG.8A. This interface allows advertisers to choose inventory according to abid price; the interface shows a list of TV schedules in particulargeographic regions and key data such as the number of estimatedimpressions as well as the current highest bid price. The list of targetTV slots can be based on user input or can be machine a generated list,and can be ranked based on a forecasted audience, or the target BidPrice.

The automated bidding system will attempt to maximize the pieces ofinventory won. When it can't win a piece of inventory, it will attemptto find a similar piece of inventory based on historical audiencecomposition. This is shown in FIG. 8B, in which an exemplary userinterface displays the outcome of a PTV bidding process: whether theslots were won, and if so the clearing price. The interface also showsthe similar inventory that was purchased instead (because of a highmatch rate with the target audience) for some of the slots that weren'twon.

Other aspects of an exemplary system include the possibility ofadvertisers to upload advertising content, select inventory, make bids,and monitor the success of a campaign as it is implemented. Othersimilar interfaces can be envisaged to allow an advertiser to placecontent in apps, and VOD environments.

Example 2: Cross-Screen Buying

A second use case is to allow advertisers to coordinate their digitaland TV inventory buys. An advertiser's target audience will view contentboth on the internet and on TV. Some advertisers will want to target thesame viewers within the same audience on both digital and TV to maximizethe frequency with which those viewers see their advertisements. Otheradvertisers may want to target different viewers within the sameaudience in order to maximize the reach, i.e., the unique number ofunique viewers seeing their advertisement.

In this case, users specify whether they want to maximize frequency orreach. Using data from the side of the advertiser's campaign thattargets digital content, as can be obtained from, e.g., web cookies orIP addresses, the system can adjust its target audience forecasts forpieces of TV inventory, which in turn, is more informative to theadvertisers.

Example 3: Look-Alike Modeling

Look-alike modeling can be used to identify a pool of consumers fromwhich a target set of TV viewers can be identified, against which aprogrammatic TV bid may be made. A population of consumers is modeled ashaving transmutable and non-transmutable characteristics. Bothtransmutable and non-transmutable characteristics are treated separatelyas macro categories. In other words, the relevant data representingeither set of characteristics is divided into two separate bundles ofinformation. Each consumer is associated with bundles of data regardingdevice behavior, categorized as either a transmutable characteristic ora non-transmutable characteristic. Each bundle is further sub-bundled byknown consumption based on those characteristics. For example, a womanknown to have had a child is represented as such within the set oftransmutable characteristics, and therefore as likely to have the needto purchase diapers. That transmutable characteristic is known to evolvewith time, meaning that in, say, two years, the purchasing tendencyindicator will adjust with the growing age of the woman's child.

The look-alike modeling can also make other assumptions by aggregatingknown characteristics within either or both transmutable andnon-transmutable categories, such as combining specific knowledge ofpurchasing history with consensus data on, for example, the person'sincome (or income bracket range). A purchasing behavior model can thenbe compared to a system generated archetype of the woman, showing likelybehavior based on known characteristics. The behavior model of thespecific user becomes more defined as more behavior data is collectedsuch as third party purchasing histories, and consent-based device usedata (such as IP address or device IDs from the manufacturer or username associations on specific web services).

With this information, advertisers can make reliable assumptions that aspecific user who is known to have characteristics X, Y and Z is alsohighly likely to engage with, for example, a specific luxury car brandadvertisement, and the like. Thus, in the example of a woman whorecently purchased diapers, it can also be deduced that the woman'shousehold income is greater than $100,000, and as information is builtup over time, the likely interest in types of purchases can be deduced.

The system can also anticipate changes in preference and adjusts to newdata received accordingly. For instance, within the category oftransmutable preferences, a change in profession, age, and maritalstatus will adjust to reflect the user's change in purchasingpreferences based on known and observable traits of similarly situatedindividuals. The system then provides inventory placement suggestions toadvertisers based on these data point adjustments. Targeting andadvertising planning is directed by a degree of data granularity that isin a continuous and synchronous state of change.

All references cited herein are incorporated by reference in theirentireties.

The foregoing description is intended to illustrate various aspects ofthe instant technology. It is not intended that the examples presentedherein limit the scope of the appended claims. The invention now beingfully described, it will be apparent to one of ordinary skill in the artthat many changes and modifications can be made thereto withoutdeparting from the spirit or scope of the appended claims.

What is claimed:
 1. A method for targeting delivery of advertising content to a population of consumers across one or more slots in television programming, the method comprising: receiving a pricepoint and one or more campaign descriptions from an advertiser, wherein each of the campaign descriptions comprises a schedule for delivery of an item of advertising content across one or more televisions accessed by a consumer in the population of consumers, and a target audience, wherein the target audience is defined by one or more demographic factors; defining a pool of consumers based on a graph of consumer properties, wherein the graph contains information about two or more TV and mobile devices used by each consumer, demographic and online behavioral data on each consumer, and similarities between pairs of consumers, and wherein the pool of consumers comprises consumers having at least a threshold similarity to a member of the target audience; receiving a list of inventory from one or more content providers, wherein the list of inventory comprises one or more TV slots; identifying one or more advertising targets, wherein each of the one or more advertising targets comprises one or more TV slots that are to be delivered within TV content identified as likely to be viewed by the pool of consumers, consistent with one or more of the campaign descriptions, and an overall cost consistent with the pricepoint; bidding on one or more of the TV slots in an advertising target; and if the bidding results in a success or a hold, instructing a media conduit to deliver the item of advertising content within the one or more TV slots to a consumer in the population of consumers on a television.
 2. The method of claim 1, wherein the bidding includes calculating a probability that the bid price will have an outcome of a win or a hold.
 3. The method of claim 1, wherein the TV content identified as likely to be viewed by the pool of consumers is based on a key performance indicator selected from audience reach and direct response.
 4. The method of claim 1, further comprising placing a further bid if the first bidding is is either a hold or a loss.
 5. The method of claim 1, wherein the advertising target includes a gross rating point.
 6. The method of claim 1, wherein the slot includes a programming time selected from: program, and daypart.
 7. The method of claim 1, wherein the slot includes a geographic indicator selected from: DMA, national, and zip-code.
 8. The method of claim 2 wherein the probability is calculated as a Bayesian distribution.
 9. A computing apparatus having at least one processor configured to execute instructions for implementing a method for targeting delivery of advertising content to a population of consumers across one or more slots in television programming, the instructions including instructions for: receiving a pricepoint and one or more campaign descriptions from an advertiser, wherein each of the campaign descriptions comprises a schedule for delivery of an item of advertising content across one or more televisions accessed by a consumer in the population of consumers, and a target audience, wherein the target audience is defined by one or more demographic factors; defining a pool of consumers based on a graph of consumer properties, wherein the graph contains information about two or more TV and mobile devices used by each consumer, demographic and online behavioral data on each consumer, and similarities between pairs of consumers, and wherein the pool of consumers comprises consumers having at least a threshold similarity to a member of the target audience; receiving a list of inventory from one or more content providers, wherein the list of inventory comprises one or more TV slots; identifying one or more advertising targets, wherein each of the one or more advertising targets comprises one or more TV slots that are to be delivered within TV content identified as likely to be viewed by the pool of consumers, consistent with one or more of the campaign descriptions, and an overall cost consistent with the pricepoint; bidding on one or more of the TV slots in an advertising target; and if the bidding results in a success or a hold, instructing a media conduit to deliver the item of advertising content within the one or more TV slots to a consumer in the population of consumers on a television.
 10. A computer-readable medium encoded with instructions for executing a method for targeting delivery of advertising content to a population of consumers across one or more slots in television programming, the instructions including instructions for receiving a pricepoint and one or more campaign descriptions from an advertiser, wherein each of the campaign descriptions comprises a schedule for delivery of an item of advertising content across one or more televisions accessed by a consumer in the population of consumers, and a target audience, wherein the target audience is defined by one or more demographic factors; defining a pool of consumers based on a graph of consumer properties, wherein the graph contains information about two or more TV and mobile devices used by each consumer, demographic and online behavioral data on each consumer, and similarities between pairs of consumers, and wherein the pool of consumers comprises consumers having at least a threshold similarity to a member of the target audience; receiving a list of inventory from one or more content providers, wherein the list of inventory comprises one or more TV slots; identifying one or more advertising targets, wherein each of the one or more advertising targets comprises one or more TV slots that are to be delivered within TV content identified as likely to be viewed by the pool of consumers, consistent with one or more of the campaign descriptions, and an overall cost consistent with the pricepoint; bidding on one or more of the TV slots in an advertising target; and if the bidding results in a success or a hold, instructing a media conduit to deliver the item of advertising content within the one or more TV slots to a consumer in the population of consumers on a television. 